{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2021/02/30\n",
    "#张赛赛\n",
    "# lstm\n",
    "# conv1d+lstm\tadam\n",
    "# conv2d+lstm\tadam\n",
    "# dou_conv1d+lstm\n",
    "# conv1d+lstm+attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential,load_model\n",
    "from tensorflow.keras.layers import LSTM,Dense,Activation,Dropout,Flatten,Conv1D,Conv2D,MaxPooling2D,Reshape,RepeatVector,MaxPooling1D,concatenate,Lambda,Multiply,Embedding,Permute,BatchNormalization\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras import optimizers,metrics\n",
    "from tensorflow.keras import Input, Model\n",
    "from tensorflow.keras import backend as k\n",
    "from tensorflow import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data():\n",
    "    file_dir = './data/train_data_1.csv'\n",
    "    all_data_frame = pd.read_csv(file_dir)\n",
    "    all_data_frame = all_data_frame[all_data_frame['ycsb']=='202106300288GF']\n",
    "    print('数据总量',all_data_frame.shape)\n",
    "    return all_data_frame\n",
    "\n",
    "def one_hot(data):\n",
    "    result = pd.get_dummies(data['weather'], sparse=True)\n",
    "    return result\n",
    "\n",
    "def scale(all_data_df):\n",
    "    #selected_features = ['hour', 'gfrl','cur_a','cur_b','cur_c','vol_a','vol_b','vol_c','p','szgl','low_tp', 'high_tp', 'avg_tp', 'wind']\n",
    "    #selected_features = ['hour', 'gfrl','cur_a','cur_b','cur_c','vol_a','vol_b','vol_c','p','szgl',\n",
    "     #                    '地面气压(hPa)', '气温2m(℃)', '地表温度(℃)', '相对湿度(%)', '风速','总云量(tcc)', '净日照强度(net,J/m2)']\n",
    "    selected_features = ['cur_a','cur_b','cur_c','p','szgl','净日照强度(net,J/m2)']\n",
    "    scaled_data = all_data_df[selected_features]\n",
    "    scaler = MinMaxScaler()\n",
    "    scaled_data = pd.DataFrame(scaler.fit_transform(scaled_data),columns=scaled_data.keys())\n",
    "    return scaled_data\n",
    "\n",
    "def label_encoder(features):\n",
    "    label_encoder = LabelEncoder()\n",
    "    for i, col in enumerate(features):\n",
    "        if features[col].dtype == 'object':\n",
    "            features[col] = label_encoder.fit_transform(np.array(features[col].astype(str)).reshape((-1,)))\n",
    "#           test_features[col] = label_encoder.transform(np.array(test_features[col].astype(str)).reshape((-1,)))\n",
    "    select_label = ['sbbm','ycsb','yhmc','weather']\n",
    "    return features[select_label]\n",
    "\n",
    "def inverse_scale(scaler,res):\n",
    "    pre_res  =scaler.inverse_transform(res)\n",
    "    return pre_res\n",
    "\n",
    "def slice_window(x_train,y_train,step):\n",
    "    X,y=[],[] \n",
    "    for i in range(0,len(x_train)-step,1): \n",
    "        end = i + step\n",
    "        oneX = x_train[i:end,:]\n",
    "        oney = y_train[end]\n",
    "        X.append(oneX)\n",
    "        y.append(oney)\n",
    "    return np.array(X),np.array(y)\n",
    "\n",
    "def data_transform(onehot_data,scale_data,all_data_df):\n",
    "    #充值索引，合并数据\n",
    "    onehot_data = onehot_data.reset_index()\n",
    "    scale_data = scale_data.reset_index()\n",
    "    #merge_data = pd.concat([onehot_data,scale_data], axis=1)\n",
    "    X_train = scale_data.drop('index',axis=1)\n",
    "    y = all_data_df['fdgl']\n",
    "    \n",
    "    #slice_window\n",
    "    step = 6\n",
    "    x_y = slice_window(np.array(X_train),np.array(y),step)\n",
    "    print('slice_window:',x_y[0].shape,x_y[1].shape)\n",
    "    X_train, y = x_y[0], x_y[1]\n",
    "    \n",
    "    #reshape参数\n",
    "#     sample = int(X_train.shape[0])//1\n",
    "#     time_step = 1\n",
    "#     feature_num = X_train.shape[1]\n",
    "#     #reshape\n",
    "#     X_train=X_train.values.reshape(sample,time_step,feature_num)\n",
    "#     y = y.values.reshape(sample,time_step)\n",
    "#     print('X:',X_train.shape)\n",
    "#     print('Y:',y.shape)\n",
    "    \n",
    "    #分割验证集\n",
    "    X_train,X_val,y_train,y_val=train_test_split(X_train,y,test_size=0.2, random_state=333)\n",
    "   \n",
    "    return [X_train,X_val,y_train,y_val]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2021 - Short-term self consumption PV plant power production forecasts"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lstm\n",
    "def lstm_model(input_shape):\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(64,activation='tanh', input_shape=(input_shape[0], input_shape[1])))\n",
    "    model.add(Dense(64,activation='relu'))\n",
    "    model.add(Dense(1,activation='relu'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 33)\n",
      "slice_window: (266, 6, 6) (266,)\n",
      "[array([[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [6.70448685e-03, 6.72182006e-03, 6.72182006e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [3.26628846e-02, 3.08514305e-02, 3.18855567e-02, 9.97774481e-01,\n",
      "         2.82597918e-02, 1.66059657e-04],\n",
      "        [8.30324910e-02, 8.01447777e-02, 8.06618407e-02, 9.22848665e-01,\n",
      "         7.93257313e-02, 1.03077483e-01],\n",
      "        [4.64672512e-01, 4.65184419e-01, 4.63116167e-01, 5.36102868e-01,\n",
      "         4.62320278e-01, 3.64563507e-01],\n",
      "        [7.06549768e-01, 7.08031713e-01, 7.05791107e-01, 2.94757666e-01,\n",
      "         7.04263758e-01, 6.22140272e-01]],\n",
      "\n",
      "       [[8.30324910e-02, 8.01447777e-02, 8.06618407e-02, 9.22848665e-01,\n",
      "         7.93257313e-02, 1.03077483e-01],\n",
      "        [4.64672512e-01, 4.65184419e-01, 4.63116167e-01, 5.36102868e-01,\n",
      "         4.62320278e-01, 3.64563507e-01],\n",
      "        [7.06549768e-01, 7.08031713e-01, 7.05791107e-01, 2.94757666e-01,\n",
      "         7.04263758e-01, 6.22140272e-01],\n",
      "        [8.24995702e-01, 8.26094450e-01, 8.25405033e-01, 1.69634026e-01,\n",
      "         8.29697571e-01, 8.11555586e-01],\n",
      "        [8.60065326e-01, 8.62805929e-01, 8.59358842e-01, 1.18941642e-01,\n",
      "         8.80515617e-01, 9.04776386e-01],\n",
      "        [8.03506962e-01, 8.04377801e-01, 8.05756636e-01, 1.62957468e-01,\n",
      "         8.36390679e-01, 8.91043863e-01]],\n",
      "\n",
      "       [[7.04830669e-03, 6.54946570e-03, 6.54946570e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [6.53257693e-03, 6.54946570e-03, 6.54946570e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [6.53257693e-03, 6.54946570e-03, 6.54946570e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [4.89370237e-02, 4.87762840e-02, 4.87475583e-02, 9.48854270e-01,\n",
      "         4.86902991e-02, 0.00000000e+00],\n",
      "        [6.36066701e-03, 6.37711134e-03, 6.54946570e-03, 9.48854270e-01,\n",
      "         4.86902991e-02, 0.00000000e+00],\n",
      "        [3.05999656e-02, 2.96449500e-02, 2.98173044e-02, 9.84668645e-01,\n",
      "         2.87555776e-02, 1.67490893e-03]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[9.06309094e-01, 9.05377456e-01, 9.06928645e-01, 1.29327399e-01,\n",
      "         8.70104115e-01, 8.66605277e-01],\n",
      "        [9.49458484e-01, 9.49327818e-01, 9.50361944e-01, 8.65479723e-02,\n",
      "         9.13237481e-01, 9.56047945e-01],\n",
      "        [8.62472065e-01, 8.64701827e-01, 8.65218890e-01, 1.53066271e-01,\n",
      "         8.46306396e-01, 9.38780637e-01],\n",
      "        [7.35258724e-01, 7.32850741e-01, 7.36814891e-01, 2.81899110e-01,\n",
      "         7.17154189e-01, 8.16005707e-01],\n",
      "        [5.12119649e-01, 5.09996553e-01, 5.12064805e-01, 4.97527201e-01,\n",
      "         5.01239465e-01, 6.04723289e-01],\n",
      "        [2.42736806e-01, 2.38883144e-01, 2.41813168e-01, 7.63353116e-01,\n",
      "         2.35250372e-01, 3.33289169e-01]],\n",
      "\n",
      "       [[7.38868833e-01, 7.39400207e-01, 7.40089624e-01, 2.49505440e-01,\n",
      "         7.49628161e-01, 8.18755346e-01],\n",
      "        [7.06549768e-01, 7.06825233e-01, 7.08204068e-01, 2.87339268e-01,\n",
      "         7.11700545e-01, 7.91932570e-01],\n",
      "        [5.59566787e-01, 5.57566356e-01, 5.59634609e-01, 4.39416419e-01,\n",
      "         5.59742191e-01, 6.88308123e-01],\n",
      "        [3.44507478e-01, 3.41433988e-01, 3.43846949e-01, 6.62215628e-01,\n",
      "         3.36390679e-01, 5.86995985e-01],\n",
      "        [1.52484098e-01, 1.49086522e-01, 1.50120648e-01, 8.55835806e-01,\n",
      "         1.43529995e-01, 3.20783233e-01],\n",
      "        [3.04280557e-02, 3.13684936e-02, 2.89555326e-02, 9.95796241e-01,\n",
      "         2.60287556e-02, 7.43572307e-02]],\n",
      "\n",
      "       [[6.53257693e-03, 6.54946570e-03, 6.54946570e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [1.05790720e-04, 1.06064222e-04, 1.06064222e-04, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [6.53257693e-03, 6.54946570e-03, 6.72182006e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [3.04280557e-02, 3.37814547e-02, 3.18855567e-02, 9.86152324e-01,\n",
      "         2.87555776e-02, 3.50518173e-03],\n",
      "        [1.36840296e-01, 1.35298173e-01, 1.37883488e-01, 8.67705242e-01,\n",
      "         1.32374814e-01, 1.45386085e-01]]]), array([[[7.98349665e-01, 7.96966563e-01, 7.99379524e-01, 2.12908012e-01,\n",
      "         3.42278136e-01, 6.87647462e-01],\n",
      "        [7.38868833e-01, 7.39400207e-01, 7.40089624e-01, 2.49505440e-01,\n",
      "         7.49628161e-01, 8.18755346e-01],\n",
      "        [7.06549768e-01, 7.06825233e-01, 7.08204068e-01, 2.87339268e-01,\n",
      "         7.11700545e-01, 7.91932570e-01],\n",
      "        [5.59566787e-01, 5.57566356e-01, 5.59634609e-01, 4.39416419e-01,\n",
      "         5.59742191e-01, 6.88308123e-01],\n",
      "        [3.44507478e-01, 3.41433988e-01, 3.43846949e-01, 6.62215628e-01,\n",
      "         3.36390679e-01, 5.86995985e-01],\n",
      "        [1.52484098e-01, 1.49086522e-01, 1.50120648e-01, 8.55835806e-01,\n",
      "         1.43529995e-01, 3.20783233e-01]],\n",
      "\n",
      "       [[3.05999656e-02, 2.96449500e-02, 2.98173044e-02, 9.84668645e-01,\n",
      "         2.87555776e-02, 1.67490893e-03],\n",
      "        [1.10538078e-01, 1.08755602e-01, 1.08066184e-01, 8.97378833e-01,\n",
      "         1.02875558e-01, 8.55201849e-02],\n",
      "        [2.87949115e-01, 2.86108239e-01, 2.87314719e-01, 7.23788328e-01,\n",
      "         2.74913237e-01, 2.24880266e-01],\n",
      "        [3.94705174e-01, 3.93829714e-01, 3.93829714e-01, 6.15974283e-01,\n",
      "         3.82498761e-01, 4.17980726e-01],\n",
      "        [4.10177067e-01, 4.10548087e-01, 4.09686315e-01, 6.06330366e-01,\n",
      "         3.92414477e-01, 5.37436969e-01],\n",
      "        [5.33264569e-01, 5.34815581e-01, 5.33264392e-01, 4.74282888e-01,\n",
      "         5.24541398e-01, 6.87545127e-01]],\n",
      "\n",
      "       [[7.85628331e-01, 7.83350569e-01, 7.85591175e-01, 1.96587537e-01,\n",
      "         8.02677243e-01, 8.36195573e-01],\n",
      "        [5.75554409e-01, 5.73767666e-01, 5.74974147e-01, 4.08753709e-01,\n",
      "         5.89985126e-01, 6.21028574e-01],\n",
      "        [2.45315455e-01, 2.43709066e-01, 2.45777318e-01, 7.52720079e-01,\n",
      "         2.46405553e-01, 3.48518702e-01],\n",
      "        [3.69606326e-02, 3.61944157e-02, 3.63667701e-02, 9.79228487e-01,\n",
      "         3.29697571e-02, 8.87371133e-02],\n",
      "        [6.53257693e-03, 6.54946570e-03, 6.54946570e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [6.70448685e-03, 6.54946570e-03, 6.72182006e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[6.53257693e-03, 6.54946570e-03, 6.72182006e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [3.21471549e-02, 3.06790762e-02, 3.10237849e-02, 9.95796241e-01,\n",
      "         2.75161130e-02, 5.99783540e-04],\n",
      "        [9.57538250e-02, 9.32437091e-02, 9.63460876e-02, 9.07764590e-01,\n",
      "         9.32077343e-02, 1.17720297e-01],\n",
      "        [5.49080282e-01, 5.47397449e-01, 5.49982765e-01, 4.60682493e-01,\n",
      "         5.37927615e-01, 3.89260897e-01],\n",
      "        [8.17431666e-01, 8.16442606e-01, 8.17304378e-01, 1.93867458e-01,\n",
      "         8.05404065e-01, 6.52501131e-01],\n",
      "        [9.68196665e-01, 9.67252671e-01, 9.68631506e-01, 3.58555885e-02,\n",
      "         9.63807635e-01, 8.44265980e-01]],\n",
      "\n",
      "       [[2.23139075e-01, 2.22337125e-01, 2.23198897e-01, 7.81651830e-01,\n",
      "         2.17154189e-01, 3.37643770e-01],\n",
      "        [3.28347946e-02, 3.24026198e-02, 3.18855567e-02, 9.86646884e-01,\n",
      "         2.82597918e-02, 8.14380897e-02],\n",
      "        [6.53257693e-03, 6.37711134e-03, 6.54946570e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [6.53257693e-03, 6.54946570e-03, 6.54946570e-03, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [1.05790720e-04, 1.06064222e-04, 1.06064222e-04, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00],\n",
      "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.97279921e-01,\n",
      "         0.00000000e+00, 0.00000000e+00]],\n",
      "\n",
      "       [[3.30067045e-02, 3.34367459e-02, 3.15408480e-02, 9.89366963e-01,\n",
      "         2.85076847e-02, 0.00000000e+00],\n",
      "        [3.30067045e-02, 3.34367459e-02, 3.15408480e-02, 9.89366963e-01,\n",
      "         2.85076847e-02, 0.00000000e+00],\n",
      "        [3.30067045e-02, 3.34367459e-02, 3.15408480e-02, 9.89366963e-01,\n",
      "         2.85076847e-02, 0.00000000e+00],\n",
      "        [4.89370237e-02, 4.87762840e-02, 4.87475583e-02, 9.48854270e-01,\n",
      "         4.86902991e-02, 0.00000000e+00],\n",
      "        [6.70448685e-03, 6.72182006e-03, 6.72182006e-03, 9.48854270e-01,\n",
      "         4.86902991e-02, 0.00000000e+00],\n",
      "        [3.26628846e-02, 3.08514305e-02, 3.18855567e-02, 9.97774481e-01,\n",
      "         2.82597918e-02, 2.97448666e-04]]]), array([1.8826e+00, 1.9314e+00, 1.2320e-01, 5.1200e-02, 7.6020e-01,\n",
      "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.6256e+00,\n",
      "       2.9600e-02, 1.1156e+00, 4.3800e-02, 1.9250e+00, 1.2308e+00,\n",
      "       7.2400e-02, 1.8722e+00, 4.0000e-04, 1.5040e-01, 1.1322e+00,\n",
      "       1.1020e+00, 1.0000e-03, 1.4802e+00, 0.0000e+00, 0.0000e+00,\n",
      "       2.1270e+00, 3.0080e-01, 2.4240e-01, 2.1380e-01, 1.9050e+00,\n",
      "       0.0000e+00, 8.8640e-01, 5.6640e-01, 9.6000e-02, 1.4600e-01,\n",
      "       3.8140e-01, 7.1400e-02, 1.1466e+00, 2.2120e-01, 6.0560e-01,\n",
      "       3.7380e-01, 0.0000e+00, 1.9764e+00, 0.0000e+00, 0.0000e+00,\n",
      "       7.8940e-01, 0.0000e+00, 3.2000e-03, 2.1334e+00, 2.2844e+00,\n",
      "       0.0000e+00, 8.9560e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
      "       7.1400e-02, 1.4160e-01, 1.9116e+00, 0.0000e+00, 5.1880e-01,\n",
      "       0.0000e+00, 1.4468e+00, 7.9160e-01, 6.5600e-02, 2.1078e+00,\n",
      "       1.6162e+00, 0.0000e+00, 1.6828e+00, 0.0000e+00, 1.9108e+00,\n",
      "       0.0000e+00, 1.4298e+00, 0.0000e+00, 0.0000e+00, 1.5064e+00,\n",
      "       2.3226e+00, 2.1616e+00, 0.0000e+00, 0.0000e+00, 1.4666e+00,\n",
      "       0.0000e+00, 1.3540e+00, 5.9688e+00, 2.1334e+00, 2.1466e+00,\n",
      "       1.8040e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.3360e-01,\n",
      "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
      "       0.0000e+00, 5.5360e-01, 0.0000e+00, 1.7040e-01, 0.0000e+00,\n",
      "       2.4382e+00, 0.0000e+00, 4.2000e-03, 2.2000e-03, 1.8000e-03,\n",
      "       2.2920e-01, 1.2794e+00, 1.6964e+00, 6.1700e-01, 1.6796e+00,\n",
      "       2.3262e+00, 2.0000e-03, 1.4004e+00, 0.0000e+00, 4.0000e-04,\n",
      "       1.1708e+00, 5.6640e-01, 1.8208e+00, 1.1272e+00, 0.0000e+00,\n",
      "       3.6040e-01, 1.0168e+00, 0.0000e+00, 1.2012e+00, 0.0000e+00,\n",
      "       3.2980e-01, 4.8780e-01, 7.2200e-02, 1.5520e-01, 2.2778e+00,\n",
      "       1.4758e+00, 9.6000e-01, 1.5576e+00, 0.0000e+00, 4.0000e-03,\n",
      "       0.0000e+00, 7.8040e-01, 2.5726e+00, 1.6250e+00, 5.8980e-01,\n",
      "       1.4004e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
      "       2.1046e+00, 0.0000e+00, 2.3582e+00, 9.8800e-02, 2.1622e+00,\n",
      "       1.4686e+00, 0.0000e+00, 4.0000e-04, 8.2520e-01, 0.0000e+00,\n",
      "       0.0000e+00, 9.7480e-01, 0.0000e+00, 0.0000e+00, 1.5266e+00,\n",
      "       0.0000e+00, 8.2040e-01, 6.7160e-01, 0.0000e+00, 8.4740e-01,\n",
      "       0.0000e+00, 0.0000e+00, 1.2420e-01, 2.1750e+00, 5.9120e-01,\n",
      "       2.5100e-01, 0.0000e+00, 1.6960e+00, 2.3400e-01, 1.6860e+00,\n",
      "       8.5800e-02, 2.1672e+00, 1.5966e+00, 1.5812e+00, 2.8000e-03,\n",
      "       1.8344e+00, 0.0000e+00, 1.6032e+00, 1.3822e+00, 5.2000e-03,\n",
      "       8.0000e-04, 0.0000e+00, 1.8914e+00, 0.0000e+00, 2.8000e-03,\n",
      "       2.0364e+00, 0.0000e+00, 8.2400e-02, 2.1142e+00, 1.8958e+00,\n",
      "       9.8460e-01, 0.0000e+00, 2.0000e-04, 8.2800e-02, 0.0000e+00,\n",
      "       6.4980e-01, 9.2400e-01, 0.0000e+00, 1.0780e-01, 5.0440e-01,\n",
      "       0.0000e+00, 1.7156e+00, 3.2160e-01, 1.2822e+00, 2.4240e-01,\n",
      "       0.0000e+00, 7.3800e-01]), array([1.2800e-01, 1.3196e+00, 0.0000e+00, 1.9684e+00, 0.0000e+00,\n",
      "       7.6080e-01, 0.0000e+00, 1.8208e+00, 1.4340e-01, 1.0678e+00,\n",
      "       2.0942e+00, 0.0000e+00, 1.9564e+00, 2.3138e+00, 7.1400e-02,\n",
      "       0.0000e+00, 1.0000e-03, 0.0000e+00, 2.2674e+00, 0.0000e+00,\n",
      "       0.0000e+00, 1.9794e+00, 0.0000e+00, 1.9732e+00, 2.3742e+00,\n",
      "       1.6210e+00, 0.0000e+00, 2.8460e-01, 1.8494e+00, 0.0000e+00,\n",
      "       7.3960e-01, 0.0000e+00, 1.6610e+00, 0.0000e+00, 0.0000e+00,\n",
      "       2.2000e-03, 5.5980e-01, 2.3020e-01, 2.2320e-01, 0.0000e+00,\n",
      "       6.3720e-01, 0.0000e+00, 0.0000e+00, 1.0194e+00, 9.4660e-01,\n",
      "       0.0000e+00, 8.0680e-01, 2.1016e+00, 2.0900e-01, 1.2362e+00,\n",
      "       1.5632e+00, 2.3874e+00, 0.0000e+00, 7.1400e-02])]\n",
      "Wall time: 182 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    #res = train_model(train_val_data,model_type[0])\n",
    "    print(train_val_data)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 单通道conv1d + lstm\n",
    "def conv1d_lstm_model(input_shape):\n",
    "    model = Sequential()\n",
    "    model.add(Conv1D(filters=128,kernel_size=3,input_shape=(input_shape[0],input_shape[1]),padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "    model.add(Conv1D(filters=64,kernel_size=3,padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "    #model.add(Conv1D(filters=64,kernel_size=2,padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "    model.add(MaxPooling1D(pool_size=2,padding='valid'))\n",
    "    #model.add(Flatten())\n",
    "    #model.add(RepeatVector(2))\n",
    "    model.add(LSTM(64,return_sequences=True))\n",
    "    model.add(LSTM(32,return_sequences=False))\n",
    "    model.add(Dense(32,activation='relu',kernel_initializer='uniform'))\n",
    "    model.add(Dense(1,activation='relu',kernel_initializer='uniform'))\n",
    "    model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    return model "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# _________________________________________________________________\n",
    "# Layer (type)                 Output Shape              Param #   \n",
    "# =================================================================\n",
    "# conv2d_10 (Conv2D)           (None, 4, 16, 5)          50        \n",
    "# _________________________________________________________________\n",
    "# flatten_24 (Flatten)         (None, 320)               0         \n",
    "# _________________________________________________________________\n",
    "# repeat_vector_22 (RepeatVect (None, 6, 320)            0         \n",
    "# _________________________________________________________________\n",
    "# lstm_36 (LSTM)               (None, 64)                98560     \n",
    "# _________________________________________________________________\n",
    "# dense_61 (Dense)             (None, 32)                2080      \n",
    "# _________________________________________________________________\n",
    "# dense_62 (Dense)             (None, 1)                 33        \n",
    "# =================================================================\n",
    "\n",
    "# 单通道conv2d + lstm\n",
    "def conv2d_lstm_model(input_shape):\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(filters=5,kernel_size=(3,3),input_shape=(input_shape[0],input_shape[1],input_shape[2]),padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "    model.add(Flatten())\n",
    "    model.add(RepeatVector(1))\n",
    "    model.add(LSTM(64,return_sequences=False))\n",
    "    model.add(Dense(32,activation='relu',kernel_initializer='uniform'))\n",
    "    model.add(Dense(1,activation='relu',kernel_initializer='uniform'))\n",
    "    return model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一种基于双通道 CNN 和 LSTM 的短期光伏功率预测方法"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "双通道卷积神经网络采用两个并行的一维CNN,每个通道各使用了两层卷积层。 两个通道选用不同尺寸的卷积核,用来提取原始光伏数据中不同尺度的特征。 提取的特征经融合层融合后再送入 LSTM 预测模型中进行预测。\n",
    "\n",
    "经不同尺寸的卷积核提取了相异尺度的高低频特征,避免了局部信息的丢失,使网络学习到了更丰富的特征,从而提升预测精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 双通道conv1d + lstm\n",
    "def dou_cnn_lstm_model(input_shape):\n",
    "    input_1 = Input(shape=(input_shape[0], input_shape[1]), name='input1')\n",
    "    \n",
    "    input_2 = Input(shape=(input_shape[0], input_shape[1]), name='input2')\n",
    "    \n",
    "    # 输入6*f 输出4*64\n",
    "    x_1 = Conv1D(filters=64,\n",
    "                       kernel_size=3,\n",
    "                       padding='valid',\n",
    "                       activation='relu',\n",
    "                       kernel_initializer='uniform')(input_1)\n",
    "    \n",
    "    #输出2*64\n",
    "    x_1 = MaxPooling1D(pool_size=2,padding='valid')(x_1)\n",
    "    \n",
    "    #输出1*32\n",
    "    x_1 = Conv1D(filters=32,\n",
    "                       kernel_size=2,\n",
    "                       padding='valid',\n",
    "                       activation='relu',\n",
    "                       kernel_initializer='uniform')(x_1)\n",
    "    \n",
    "    #输出2*32\n",
    "    x_2 = Conv1D(filters=32,\n",
    "                       kernel_size=5,\n",
    "                       padding='valid',\n",
    "                       activation='relu',\n",
    "                       kernel_initializer='uniform')(input_2)\n",
    "    \n",
    "    #输出1*32\n",
    "    x_2 = MaxPooling1D(pool_size=2,padding='valid')(x_2)\n",
    "\n",
    "    x = concatenate([x_1, x_2])\n",
    "\n",
    "    x = Flatten()(x)\n",
    "    \n",
    "    x = RepeatVector(1)(x)\n",
    "    \n",
    "    x = LSTM(64,return_sequences=True)(x)\n",
    "    \n",
    "    x = LSTM(32,return_sequences=False)(x)\n",
    "    \n",
    "    x = Dense(32,activation='relu',kernel_initializer='uniform')(x)\n",
    "    \n",
    "    output_ = Dense(1,activation='relu',kernel_initializer='uniform')(x)\n",
    "    \n",
    "    model = Model(inputs=[input_1, input_2], outputs=[output_])\n",
    "    \n",
    "    model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    \n",
    "    return model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2021 - Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于注意力机制具有较强的特征提取能力，设计了注意力模块来捕获输入时间序列的不同序列之间的依赖关系和不同时间步长之间的关系。然后，获得PV功率的预测。以上三个模块主要由CNN - LSTM - Attention组成"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The attention network in Fig. 4 comprise two full connection layers and a Softmax function that outputs a 1 2 vector represented bywi ¼½ws i ;wl i . The two elements of the vector represent the weights assigned to the predicted value. Therefore, by calculating the weighted value of the two vectors, the ﬁnal value of predicted PV power can be obtained.\n",
    "\n",
    "where the hs i and hl long-term temporal modules; the ws i and wl (9) i represent the hidden state of short-term and i represent the weights assigned to the target forecasting of yi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attention(inputs,feature_cnt,dim):\n",
    "    \n",
    "    a = Flatten()(inputs)\n",
    "    \n",
    "    a = Dense(feature_cnt*dim,activation='softmax')(a)\n",
    "    \n",
    "    a = Reshape((dim,feature_cnt,))(a)\n",
    "    \n",
    "    a = Lambda(lambda x: k.sum(x, axis=1), name='attention')(a)\n",
    "    \n",
    "    a = RepeatVector(dim)(a)\n",
    "    \n",
    "    #a = Permute((2, 1), name='attention_vec')(a)\n",
    "    \n",
    "    attention_out = Multiply()([inputs, a])\n",
    "    \n",
    "    return attention_out\n",
    "\n",
    "# 双通道cnn_lstm + attention\n",
    "def cnn_lstm_attention(input_shape):\n",
    "    input_1 = Input(shape=(input_shape[0], input_shape[1]), name='input1')\n",
    "    input_2 = Input(shape=(input_shape[0], input_shape[1]), name='input2')\n",
    "    \n",
    "    # 输出4*32\n",
    "    x_1 = Conv1D(filters=32,\n",
    "                       kernel_size=3,\n",
    "                       padding='valid',\n",
    "                       activation='relu',\n",
    "                       kernel_initializer='uniform')(input_1)\n",
    "    # 输出2*32\n",
    "    x_1 = Conv1D(filters=32,\n",
    "                       kernel_size=3,\n",
    "                       padding='valid',\n",
    "                       activation='relu',\n",
    "                       kernel_initializer='uniform')(x_1)\n",
    "    \n",
    "    #输出1*32\n",
    "    x_1 = MaxPooling1D(pool_size=2,padding='valid')(x_1)\n",
    "    \n",
    "    # 输出1*32\n",
    "    x_1 = LSTM(32,return_sequences=False)(x_1)\n",
    "    \n",
    "    #输出1*32\n",
    "    x_2 = Conv1D(filters=32,\n",
    "                       kernel_size=6,\n",
    "                       padding='valid',\n",
    "                       activation='relu',\n",
    "                       kernel_initializer='uniform')(input_2) \n",
    "    # 输出1*32\n",
    "    x_2 = LSTM(32,return_sequences=False)(x_2)\n",
    "    \n",
    "    # 输出3*64\n",
    "    x = concatenate([x_1, x_2])\n",
    "    \n",
    "#     x = Flatten()(x)\n",
    "    \n",
    "#     x = RepeatVector(6)(x)\n",
    "#     print(x)\n",
    "    \n",
    "    # 输出6*32\n",
    "    x = attention(x,64,1)\n",
    "    \n",
    "    x = Flatten()(x)\n",
    "    \n",
    "    x = Dense(64,activation='relu',kernel_initializer='uniform')(x)\n",
    "    \n",
    "    output_ = Dense(1,activation='relu',kernel_initializer='uniform')(x)\n",
    "    \n",
    "    model = Model(inputs=[input_1, input_2], outputs=[output_])\n",
    "    \n",
    "    model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    \n",
    "    return model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验:conv2d_lstm_attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 双通道conv2d + attention\n",
    "# def conv2d_lstm_attention(input_shape):\n",
    "#     input_ = Input(shape=(input_shape[0],input_shape[1],input_shape[2]), name='inputs')\n",
    "      \n",
    "#     # 输出4*32\n",
    "#     x = Conv2D(filters=4,\n",
    "#                  kernel_size=(3,3),\n",
    "#                  padding='valid',\n",
    "#                  activation='relu',\n",
    "#                  kernel_initializer='uniform')(input_)\n",
    "    \n",
    "#     x = Flatten()(x)\n",
    "    \n",
    "#     x = RepeatVector(1)(x)\n",
    "\n",
    "#     # 输出6*32\n",
    "#     x = attention(x,304,1)\n",
    "    \n",
    "#     x = Flatten()(x)\n",
    "    \n",
    "#     x = RepeatVector(3)(x)\n",
    "    \n",
    "#     x = LSTM(128,return_sequences=True)(x)\n",
    "    \n",
    "#     x = LSTM(64,return_sequences=False)(x)\n",
    "#     x = Dropout(0.2)(x)\n",
    "#     x = Dense(64,activation='relu',kernel_initializer='uniform')(x)\n",
    "    \n",
    "#     output_ = Dense(1,activation='relu',kernel_initializer='uniform')(x)\n",
    "    \n",
    "#     model = Model(inputs=input_, outputs=output_)\n",
    "    \n",
    "#     model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    \n",
    "#     return model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 双通道conv2d + attention\n",
    "def conv2d_lstm_attention(input_shape):\n",
    "    input_ = Input(shape=(input_shape[0],input_shape[1],input_shape[2]), name='inputs')\n",
    "    \n",
    "    # 输出4*32\n",
    "    x = Conv2D(filters=4,\n",
    "                 kernel_size=(3,3),\n",
    "                 padding='valid',\n",
    "                 activation='relu',\n",
    "                 kernel_initializer='uniform')(input_)\n",
    "   # print(x)\n",
    "    x = Flatten()(x)\n",
    "    \n",
    "    x = RepeatVector(1)(x)\n",
    "\n",
    "    # 输出6*32\n",
    "    x = attention(x,64,1)\n",
    "    \n",
    "    x = Flatten()(x)\n",
    "    \n",
    "    x = RepeatVector(6)(x)\n",
    "    \n",
    "    x = LSTM(128,return_sequences=True)(x)\n",
    "    \n",
    "    x = LSTM(64,return_sequences=False)(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    x = Dense(64,activation='relu',kernel_initializer='uniform')(x)\n",
    "    \n",
    "    output_ = Dense(1,activation='relu',kernel_initializer='uniform')(x)\n",
    "    \n",
    "    model = Model(inputs=input_, outputs=output_)\n",
    "    \n",
    "    model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    \n",
    "    return model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attention(inputs,feature_cnt,dim):\n",
    "    \n",
    "    a = Flatten()(inputs)\n",
    "    \n",
    "    a = Dense(feature_cnt*dim,activation='softmax')(a)\n",
    "    \n",
    "    a = Reshape((dim,feature_cnt,))(a)\n",
    "    \n",
    "    #a = Lambda(lambda x: k.sum(x, axis=1), name='attention')(a)\n",
    "    \n",
    "     #a = RepeatVector(dim)(a)\n",
    "    \n",
    "    #a = Permute((2, 1), name='attention_vec')(a)\n",
    "    \n",
    "    attention_out = Multiply()([inputs, a])\n",
    "    \n",
    "    return attention_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(train_val_data,model_type):\n",
    "    #获取数据\n",
    "    X_train,X_val,y_train,y_val = train_val_data[0],train_val_data[1],train_val_data[2],train_val_data[3]\n",
    "    print('训练数据总量',X_train.shape)\n",
    "    print('测试数据总量',X_val.shape)\n",
    "    #超参数\n",
    "    time_step = X_train.shape[1]\n",
    "    feature_nums = X_train.shape[2]\n",
    "    epochs=2000\n",
    "    batch_size=16\n",
    "    patience=10\n",
    "    print('time_step:',time_step,'feature_nums:',feature_nums)\n",
    "    \n",
    "###########################################################################\n",
    "    if model_type =='lstm':\n",
    "        # LSTM \n",
    "        model = lstm_model([time_step,feature_nums])\n",
    "    if model_type =='conv1d_lstm':\n",
    "        # Conv1d-LSTM\n",
    "        model = conv1d_lstm_model([time_step,feature_nums])\n",
    "    if model_type =='conv2d_lstm':\n",
    "        # Conv2d-LSTM\n",
    "        model = conv2d_lstm_model([time_step,feature_nums,1])\n",
    "        X_train  = X_train.reshape(212, 6, 18, 1)\n",
    "        X_val  = X_val.reshape(54, 6, 18, 1)\n",
    "        \n",
    "    if model_type =='conv2d_lstm_att':\n",
    "        # Conv2d-LSTM\n",
    "        model = conv2d_lstm_attention([time_step,feature_nums,1])\n",
    "        X_train  = X_train.reshape(212, 6, 6, 1)\n",
    "        X_val  = X_val.reshape(54, 6, 6, 1)\n",
    "    \n",
    "    if model_type =='dou_cnn_lstm':\n",
    "        # double-Conv1d-LSTM\n",
    "        model = dou_cnn_lstm_model([time_step,feature_nums])\n",
    "    \n",
    "    if model_type =='cnn_lstm_att':\n",
    "        # cnn_lstm_attention\n",
    "        model = cnn_lstm_attention([time_step,feature_nums])\n",
    "###########################################################################\n",
    "\n",
    "    print(model.summary())\n",
    "    \n",
    "    # 转为tensor\n",
    "    X_train = k.cast_to_floatx(X_train)\n",
    "    y_train = k.cast_to_floatx(y_train)\n",
    "    X_val = k.cast_to_floatx(X_val)\n",
    "    y_val = k.cast_to_floatx(y_val)\n",
    "    \n",
    "    # 优化器\n",
    "    adam = optimizers.Adam(learning_rate = 0.0001)\n",
    "    #adam = optimizers.SGD(learning_rate = 0.001,momentum=0.2)\n",
    "    \n",
    "    # 损失函数\n",
    "    model.compile(loss='mse', optimizer=adam)\n",
    "    \n",
    "    #early_stop\n",
    "    early_stopping = EarlyStopping(monitor='val_loss', patience=patience, verbose=1)\n",
    "    \n",
    "    # 超参数\n",
    "    history = model.fit([X_train,X_train],y_train, \n",
    "                        epochs=epochs,\n",
    "                        batch_size=batch_size,\n",
    "                        validation_split=0.2,\n",
    "                        verbose=1,\n",
    "                        callbacks=[early_stopping], \n",
    "                        shuffle=False)\n",
    "    \n",
    "    y_val_pred = model.predict([X_val,X_val])\n",
    "    rmse = mean_squared_error(y_val, y_val_pred) ** 0.5\n",
    "    score=1.0/(1.0+rmse)\n",
    "    print('score:',score)\n",
    "    print('rmse:',rmse)\n",
    "    #model.save('./model/lstm_two')\n",
    "    draw(history)\n",
    "    return [y_val,y_val_pred]\n",
    "\n",
    "def draw(history):\n",
    "    from matplotlib import pyplot\n",
    "    pyplot.plot(history.history['loss'], label='train')\n",
    "    pyplot.plot(history.history['val_loss'], label='test')\n",
    "    pyplot.legend()\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# __________________________________________________________________________________________________\n",
    "# Layer (type)                    Output Shape         Param #     Connected to                     \n",
    "# ==================================================================================================\n",
    "# inputs (InputLayer)             [(None, 6, 18, 1)]   0                                            \n",
    "# __________________________________________________________________________________________________\n",
    "# conv2d_18 (Conv2D)              (None, 4, 16, 8)     80          inputs[0][0]                     \n",
    "# __________________________________________________________________________________________________\n",
    "# flatten_49 (Flatten)            (None, 512)          0           conv2d_18[0][0]                  \n",
    "# __________________________________________________________________________________________________\n",
    "# repeat_vector_41 (RepeatVector) (None, 1, 512)       0           flatten_49[0][0]                 \n",
    "# __________________________________________________________________________________________________\n",
    "# flatten_50 (Flatten)            (None, 512)          0           repeat_vector_41[0][0]           \n",
    "# __________________________________________________________________________________________________\n",
    "# dense_90 (Dense)                (None, 512)          262656      flatten_50[0][0]                 \n",
    "# __________________________________________________________________________________________________\n",
    "# reshape_18 (Reshape)            (None, 1, 512)       0           dense_90[0][0]                   \n",
    "# __________________________________________________________________________________________________\n",
    "# attention (Lambda)              (None, 512)          0           reshape_18[0][0]                 \n",
    "# __________________________________________________________________________________________________\n",
    "# repeat_vector_42 (RepeatVector) (None, 1, 512)       0           attention[0][0]                  \n",
    "# __________________________________________________________________________________________________\n",
    "# multiply_18 (Multiply)          (None, 1, 512)       0           repeat_vector_41[0][0]           \n",
    "#                                                                  repeat_vector_42[0][0]           \n",
    "# __________________________________________________________________________________________________\n",
    "# lstm_50 (LSTM)                  (None, 64)           147712      multiply_18[0][0]                \n",
    "# __________________________________________________________________________________________________\n",
    "# dense_91 (Dense)                (None, 64)           4160        lstm_50[0][0]                    \n",
    "# __________________________________________________________________________________________________\n",
    "# dense_92 (Dense)                (None, 1)            65          dense_91[0][0]                   \n",
    "# =================================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "score: 0.7881757823328066\n",
    "rmse: 0.2687525072646177"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 33)\n",
      "slice_window: (266, 6, 6) (266,)\n",
      "Wall time: 365 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212, 6, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_val_data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.8826e+00, 1.9314e+00, 1.2320e-01, 5.1200e-02, 7.6020e-01,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.6256e+00,\n",
       "       2.9600e-02, 1.1156e+00, 4.3800e-02, 1.9250e+00, 1.2308e+00,\n",
       "       7.2400e-02, 1.8722e+00, 4.0000e-04, 1.5040e-01, 1.1322e+00,\n",
       "       1.1020e+00, 1.0000e-03, 1.4802e+00, 0.0000e+00, 0.0000e+00,\n",
       "       2.1270e+00, 3.0080e-01, 2.4240e-01, 2.1380e-01, 1.9050e+00,\n",
       "       0.0000e+00, 8.8640e-01, 5.6640e-01, 9.6000e-02, 1.4600e-01,\n",
       "       3.8140e-01, 7.1400e-02, 1.1466e+00, 2.2120e-01, 6.0560e-01,\n",
       "       3.7380e-01, 0.0000e+00, 1.9764e+00, 0.0000e+00, 0.0000e+00,\n",
       "       7.8940e-01, 0.0000e+00, 3.2000e-03, 2.1334e+00, 2.2844e+00,\n",
       "       0.0000e+00, 8.9560e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       7.1400e-02, 1.4160e-01, 1.9116e+00, 0.0000e+00, 5.1880e-01,\n",
       "       0.0000e+00, 1.4468e+00, 7.9160e-01, 6.5600e-02, 2.1078e+00,\n",
       "       1.6162e+00, 0.0000e+00, 1.6828e+00, 0.0000e+00, 1.9108e+00,\n",
       "       0.0000e+00, 1.4298e+00, 0.0000e+00, 0.0000e+00, 1.5064e+00,\n",
       "       2.3226e+00, 2.1616e+00, 0.0000e+00, 0.0000e+00, 1.4666e+00,\n",
       "       0.0000e+00, 1.3540e+00, 5.9688e+00, 2.1334e+00, 2.1466e+00,\n",
       "       1.8040e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.3360e-01,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 5.5360e-01, 0.0000e+00, 1.7040e-01, 0.0000e+00,\n",
       "       2.4382e+00, 0.0000e+00, 4.2000e-03, 2.2000e-03, 1.8000e-03,\n",
       "       2.2920e-01, 1.2794e+00, 1.6964e+00, 6.1700e-01, 1.6796e+00,\n",
       "       2.3262e+00, 2.0000e-03, 1.4004e+00, 0.0000e+00, 4.0000e-04,\n",
       "       1.1708e+00, 5.6640e-01, 1.8208e+00, 1.1272e+00, 0.0000e+00,\n",
       "       3.6040e-01, 1.0168e+00, 0.0000e+00, 1.2012e+00, 0.0000e+00,\n",
       "       3.2980e-01, 4.8780e-01, 7.2200e-02, 1.5520e-01, 2.2778e+00,\n",
       "       1.4758e+00, 9.6000e-01, 1.5576e+00, 0.0000e+00, 4.0000e-03,\n",
       "       0.0000e+00, 7.8040e-01, 2.5726e+00, 1.6250e+00, 5.8980e-01,\n",
       "       1.4004e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       2.1046e+00, 0.0000e+00, 2.3582e+00, 9.8800e-02, 2.1622e+00,\n",
       "       1.4686e+00, 0.0000e+00, 4.0000e-04, 8.2520e-01, 0.0000e+00,\n",
       "       0.0000e+00, 9.7480e-01, 0.0000e+00, 0.0000e+00, 1.5266e+00,\n",
       "       0.0000e+00, 8.2040e-01, 6.7160e-01, 0.0000e+00, 8.4740e-01,\n",
       "       0.0000e+00, 0.0000e+00, 1.2420e-01, 2.1750e+00, 5.9120e-01,\n",
       "       2.5100e-01, 0.0000e+00, 1.6960e+00, 2.3400e-01, 1.6860e+00,\n",
       "       8.5800e-02, 2.1672e+00, 1.5966e+00, 1.5812e+00, 2.8000e-03,\n",
       "       1.8344e+00, 0.0000e+00, 1.6032e+00, 1.3822e+00, 5.2000e-03,\n",
       "       8.0000e-04, 0.0000e+00, 1.8914e+00, 0.0000e+00, 2.8000e-03,\n",
       "       2.0364e+00, 0.0000e+00, 8.2400e-02, 2.1142e+00, 1.8958e+00,\n",
       "       9.8460e-01, 0.0000e+00, 2.0000e-04, 8.2800e-02, 0.0000e+00,\n",
       "       6.4980e-01, 9.2400e-01, 0.0000e+00, 1.0780e-01, 5.0440e-01,\n",
       "       0.0000e+00, 1.7156e+00, 3.2160e-01, 1.2822e+00, 2.4240e-01,\n",
       "       0.0000e+00, 7.3800e-01])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_val_data[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 33)\n",
      "slice_window: (266, 6, 6) (266,)\n",
      "训练数据总量 (212, 6, 6)\n",
      "测试数据总量 (54, 6, 6)\n",
      "time_step: 6 feature_nums: 6\n",
      "Model: \"model_4\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "inputs (InputLayer)             [(None, 6, 6, 1)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 4, 4, 4)      40          inputs[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "flatten_16 (Flatten)            (None, 64)           0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_16 (RepeatVector) (None, 1, 64)        0           flatten_16[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "flatten_17 (Flatten)            (None, 64)           0           repeat_vector_16[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_18 (Dense)                (None, 64)           4160        flatten_17[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_6 (Reshape)             (None, 1, 64)        0           dense_18[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "attention (Lambda)              (None, 64)           0           reshape_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_17 (RepeatVector) (None, 1, 64)        0           attention[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_6 (Multiply)           (None, 1, 64)        0           repeat_vector_16[0][0]           \n",
      "                                                                 repeat_vector_17[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "flatten_18 (Flatten)            (None, 64)           0           multiply_6[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_18 (RepeatVector) (None, 6, 64)        0           flatten_18[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lstm_11 (LSTM)                  (None, 6, 128)       98816       repeat_vector_18[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "lstm_12 (LSTM)                  (None, 64)           49408       lstm_11[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 64)           0           lstm_12[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_19 (Dense)                (None, 64)           4160        dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_20 (Dense)                (None, 1)            65          dense_19[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 156,649\n",
      "Trainable params: 156,649\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "Train on 169 samples, validate on 43 samples\n",
      "Epoch 1/2000\n",
      "169/169 [==============================] - 3s 19ms/sample - loss: 1.3271 - val_loss: 1.0991\n",
      "Epoch 2/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3238 - val_loss: 1.0955\n",
      "Epoch 3/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3199 - val_loss: 1.0913\n",
      "Epoch 4/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3151 - val_loss: 1.0858\n",
      "Epoch 5/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3088 - val_loss: 1.0781\n",
      "Epoch 6/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2996 - val_loss: 1.0667\n",
      "Epoch 7/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2862 - val_loss: 1.0489\n",
      "Epoch 8/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2646 - val_loss: 1.0205\n",
      "Epoch 9/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2275 - val_loss: 0.9738\n",
      "Epoch 10/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.1668 - val_loss: 0.8985\n",
      "Epoch 11/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.0769 - val_loss: 0.7882\n",
      "Epoch 12/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.9590 - val_loss: 0.6660\n",
      "Epoch 13/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8441 - val_loss: 0.6007\n",
      "Epoch 14/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8090 - val_loss: 0.6009\n",
      "Epoch 15/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8007 - val_loss: 0.5979\n",
      "Epoch 16/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8212 - val_loss: 0.5938\n",
      "Epoch 17/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8260 - val_loss: 0.5924\n",
      "Epoch 18/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8128 - val_loss: 0.5910\n",
      "Epoch 19/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8087 - val_loss: 0.5888\n",
      "Epoch 20/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8175 - val_loss: 0.5864\n",
      "Epoch 21/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7809 - val_loss: 0.5841\n",
      "Epoch 22/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8186 - val_loss: 0.5818\n",
      "Epoch 23/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8068 - val_loss: 0.5793\n",
      "Epoch 24/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8068 - val_loss: 0.5766\n",
      "Epoch 25/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7948 - val_loss: 0.5738\n",
      "Epoch 26/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7829 - val_loss: 0.5705\n",
      "Epoch 27/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7805 - val_loss: 0.5670\n",
      "Epoch 28/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7805 - val_loss: 0.5632\n",
      "Epoch 29/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7659 - val_loss: 0.5590\n",
      "Epoch 30/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7768 - val_loss: 0.5543\n",
      "Epoch 31/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7689 - val_loss: 0.5492\n",
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     ]
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     "text": [
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      "Epoch 199/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2199 - val_loss: 0.0227\n",
      "Epoch 200/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2102 - val_loss: 0.0245\n",
      "Epoch 201/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1881 - val_loss: 0.0231\n",
      "Epoch 202/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1965 - val_loss: 0.0219\n",
      "Epoch 203/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2061 - val_loss: 0.0215\n",
      "Epoch 204/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2112 - val_loss: 0.0215\n",
      "Epoch 205/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1701 - val_loss: 0.0228\n",
      "Epoch 206/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2343 - val_loss: 0.0223\n",
      "Epoch 207/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1961 - val_loss: 0.0214\n",
      "Epoch 208/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2235 - val_loss: 0.0211\n",
      "Epoch 209/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2139 - val_loss: 0.0226\n",
      "Epoch 210/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2052 - val_loss: 0.0220\n",
      "Epoch 211/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2252 - val_loss: 0.0211\n",
      "Epoch 212/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2257 - val_loss: 0.0216\n",
      "Epoch 213/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1903 - val_loss: 0.0225\n",
      "Epoch 214/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2158 - val_loss: 0.0207\n",
      "Epoch 215/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2161 - val_loss: 0.0206\n",
      "Epoch 216/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2318 - val_loss: 0.0231\n",
      "Epoch 217/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2049 - val_loss: 0.0221\n",
      "Epoch 218/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2148 - val_loss: 0.0212\n",
      "Epoch 219/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2140 - val_loss: 0.0202\n",
      "Epoch 220/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1828 - val_loss: 0.0197\n",
      "Epoch 221/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2181 - val_loss: 0.0197\n",
      "Epoch 222/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2264 - val_loss: 0.0197\n",
      "Epoch 223/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2369 - val_loss: 0.0204\n",
      "Epoch 224/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2176 - val_loss: 0.0206\n",
      "Epoch 225/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2162 - val_loss: 0.0193\n",
      "Epoch 226/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2005 - val_loss: 0.0189\n",
      "Epoch 227/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1952 - val_loss: 0.0191\n",
      "Epoch 228/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2230 - val_loss: 0.0188\n",
      "Epoch 229/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1996 - val_loss: 0.0184\n",
      "Epoch 230/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2157 - val_loss: 0.0183\n",
      "Epoch 231/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2350 - val_loss: 0.0196\n",
      "Epoch 232/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2011 - val_loss: 0.0200\n",
      "Epoch 233/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2118 - val_loss: 0.0194\n",
      "Epoch 234/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1950 - val_loss: 0.0185\n",
      "Epoch 235/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2297 - val_loss: 0.0184\n",
      "Epoch 236/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2093 - val_loss: 0.0184\n",
      "Epoch 237/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2170 - val_loss: 0.0188\n",
      "Epoch 238/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2248 - val_loss: 0.0182\n",
      "Epoch 239/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2067 - val_loss: 0.0179\n",
      "Epoch 240/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2152 - val_loss: 0.0176\n",
      "Epoch 241/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2129 - val_loss: 0.0188\n",
      "Epoch 242/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2164 - val_loss: 0.0179\n",
      "Epoch 243/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2182 - val_loss: 0.0186\n",
      "Epoch 244/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1973 - val_loss: 0.0182\n",
      "Epoch 245/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2507 - val_loss: 0.0186\n",
      "Epoch 246/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2226 - val_loss: 0.0183\n",
      "Epoch 247/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2191 - val_loss: 0.0177\n",
      "Epoch 248/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2116 - val_loss: 0.0175\n",
      "Epoch 249/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2406 - val_loss: 0.0178\n",
      "Epoch 250/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1872 - val_loss: 0.0172\n",
      "Epoch 251/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1727 - val_loss: 0.0172\n",
      "Epoch 252/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2161 - val_loss: 0.0181\n",
      "Epoch 253/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2058 - val_loss: 0.0178\n",
      "Epoch 254/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1859 - val_loss: 0.0173\n",
      "Epoch 255/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1974 - val_loss: 0.0168\n",
      "Epoch 256/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2214 - val_loss: 0.0168\n",
      "Epoch 257/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1987 - val_loss: 0.0169\n",
      "Epoch 258/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2056 - val_loss: 0.0169\n",
      "Epoch 259/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1945 - val_loss: 0.0166\n",
      "Epoch 260/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1903 - val_loss: 0.0163\n",
      "Epoch 261/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2071 - val_loss: 0.0162\n",
      "Epoch 262/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1974 - val_loss: 0.0159\n",
      "Epoch 263/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1980 - val_loss: 0.0158\n",
      "Epoch 264/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2043 - val_loss: 0.0156\n",
      "Epoch 265/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2092 - val_loss: 0.0155\n",
      "Epoch 266/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2065 - val_loss: 0.0154\n",
      "Epoch 267/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1922 - val_loss: 0.0154\n",
      "Epoch 268/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1895 - val_loss: 0.0154\n",
      "Epoch 269/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2098 - val_loss: 0.0156\n",
      "Epoch 270/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2125 - val_loss: 0.0158\n",
      "Epoch 271/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1815 - val_loss: 0.0154\n",
      "Epoch 272/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1847 - val_loss: 0.0153\n",
      "Epoch 273/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2173 - val_loss: 0.0175\n",
      "Epoch 274/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2279 - val_loss: 0.0173\n",
      "Epoch 275/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2269 - val_loss: 0.0162\n",
      "Epoch 276/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2008 - val_loss: 0.0160\n",
      "Epoch 277/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2081 - val_loss: 0.0164\n",
      "Epoch 278/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1818 - val_loss: 0.0162\n",
      "Epoch 279/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2239 - val_loss: 0.0158\n",
      "Epoch 280/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2288 - val_loss: 0.0163\n",
      "Epoch 281/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1900 - val_loss: 0.0159\n",
      "Epoch 282/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2061 - val_loss: 0.0160\n",
      "Epoch 00282: early stopping\n",
      "score: 0.7706229484489419\n",
      "rmse: 0.2976514675727382\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    res = train_model(train_val_data,model_type[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 33)\n",
      "slice_window: (266, 6, 21) (266,)\n",
      "训练数据总量 (212, 6, 21)\n",
      "测试数据总量 (54, 6, 21)\n",
      "time_step: 6 feature_nums: 21\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "inputs (InputLayer)             [(None, 6, 21, 1)]   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 4, 19, 4)     40          inputs[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 304)          0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_2 (RepeatVector)  (None, 1, 304)       0           flatten_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 304)          0           repeat_vector_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 304)          92720       flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 1, 304)       0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "attention (Lambda)              (None, 304)          0           reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_3 (RepeatVector)  (None, 1, 304)       0           attention[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)           (None, 1, 304)       0           repeat_vector_2[0][0]            \n",
      "                                                                 repeat_vector_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 304)          0           multiply_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_4 (RepeatVector)  (None, 3, 304)       0           flatten_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     (None, 3, 128)       221696      repeat_vector_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   (None, 64)           49408       lstm[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 64)           0           lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 64)           4160        dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1)            65          dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 368,089\n",
      "Trainable params: 368,089\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "Train on 169 samples, validate on 43 samples\n",
      "WARNING:tensorflow:From D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Epoch 1/2000\n",
      "169/169 [==============================] - 7s 39ms/sample - loss: 1.3271 - val_loss: 1.0991\n",
      "Epoch 2/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3239 - val_loss: 1.0957\n",
      "Epoch 3/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3201 - val_loss: 1.0916\n",
      "Epoch 4/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3157 - val_loss: 1.0868\n",
      "Epoch 5/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3103 - val_loss: 1.0808\n",
      "Epoch 6/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3037 - val_loss: 1.0732\n",
      "Epoch 7/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2949 - val_loss: 1.0633\n",
      "Epoch 8/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2834 - val_loss: 1.0500\n",
      "Epoch 9/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2683 - val_loss: 1.0322\n",
      "Epoch 10/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2477 - val_loss: 1.0082\n",
      "Epoch 11/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.2201 - val_loss: 0.9763\n",
      "Epoch 12/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.1841 - val_loss: 0.9342\n",
      "Epoch 13/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.1361 - val_loss: 0.8805\n",
      "Epoch 14/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.0758 - val_loss: 0.8153\n",
      "Epoch 15/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.0051 - val_loss: 0.7428\n",
      "Epoch 16/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.9260 - val_loss: 0.6740\n",
      "Epoch 17/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8764 - val_loss: 0.6257\n",
      "Epoch 18/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8250 - val_loss: 0.6052\n",
      "Epoch 19/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8150 - val_loss: 0.6020\n",
      "Epoch 20/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8225 - val_loss: 0.6012\n",
      "Epoch 21/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8324 - val_loss: 0.5993\n",
      "Epoch 22/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8125 - val_loss: 0.5976\n",
      "Epoch 23/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8130 - val_loss: 0.5959\n",
      "Epoch 24/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8000 - val_loss: 0.5938\n",
      "Epoch 25/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8049 - val_loss: 0.5913\n",
      "Epoch 26/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8072 - val_loss: 0.5884\n",
      "Epoch 27/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.8078 - val_loss: 0.5849\n",
      "Epoch 28/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7875 - val_loss: 0.5808\n",
      "Epoch 29/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7885 - val_loss: 0.5758\n",
      "Epoch 30/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7802 - val_loss: 0.5698\n",
      "Epoch 31/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7745 - val_loss: 0.5625\n",
      "Epoch 32/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7729 - val_loss: 0.5539\n",
      "Epoch 33/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7643 - val_loss: 0.5439\n",
      "Epoch 34/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7461 - val_loss: 0.5324\n",
      "Epoch 35/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7308 - val_loss: 0.5194\n",
      "Epoch 36/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.7081 - val_loss: 0.5049\n",
      "Epoch 37/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.6966 - val_loss: 0.4889\n",
      "Epoch 38/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.6702 - val_loss: 0.4715\n",
      "Epoch 39/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.6649 - val_loss: 0.4530\n",
      "Epoch 40/2000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1881 - val_loss: 0.0234\n",
      "Epoch 209/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1903 - val_loss: 0.0234\n",
      "Epoch 210/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1903 - val_loss: 0.0231\n",
      "Epoch 211/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1844 - val_loss: 0.0227\n",
      "Epoch 212/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1951 - val_loss: 0.0225\n",
      "Epoch 213/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1997 - val_loss: 0.0225\n",
      "Epoch 214/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1763 - val_loss: 0.0230\n",
      "Epoch 215/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1841 - val_loss: 0.0227\n",
      "Epoch 216/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1922 - val_loss: 0.0224\n",
      "Epoch 217/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1889 - val_loss: 0.0227\n",
      "Epoch 218/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2007 - val_loss: 0.0225\n",
      "Epoch 219/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1903 - val_loss: 0.0226\n",
      "Epoch 220/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1763 - val_loss: 0.0225\n",
      "Epoch 221/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1849 - val_loss: 0.0224\n",
      "Epoch 222/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1894 - val_loss: 0.0224\n",
      "Epoch 223/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1777 - val_loss: 0.0223\n",
      "Epoch 224/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1719 - val_loss: 0.0224\n",
      "Epoch 225/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1810 - val_loss: 0.0222\n",
      "Epoch 226/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1793 - val_loss: 0.0222\n",
      "Epoch 227/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1907 - val_loss: 0.0217\n",
      "Epoch 228/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1810 - val_loss: 0.0216\n",
      "Epoch 229/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1981 - val_loss: 0.0216\n",
      "Epoch 230/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1859 - val_loss: 0.0220\n",
      "Epoch 231/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1881 - val_loss: 0.0220\n",
      "Epoch 232/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1864 - val_loss: 0.0218\n",
      "Epoch 233/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1804 - val_loss: 0.0219\n",
      "Epoch 234/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1917 - val_loss: 0.0221\n",
      "Epoch 235/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1936 - val_loss: 0.0220\n",
      "Epoch 236/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2065 - val_loss: 0.0215\n",
      "Epoch 237/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1930 - val_loss: 0.0216\n",
      "Epoch 238/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1868 - val_loss: 0.0216\n",
      "Epoch 239/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1891 - val_loss: 0.0217\n",
      "Epoch 240/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1882 - val_loss: 0.0218\n",
      "Epoch 241/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1922 - val_loss: 0.0217\n",
      "Epoch 242/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1858 - val_loss: 0.0217\n",
      "Epoch 243/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1826 - val_loss: 0.0216\n",
      "Epoch 244/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1901 - val_loss: 0.0217\n",
      "Epoch 245/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1931 - val_loss: 0.0218\n",
      "Epoch 246/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1898 - val_loss: 0.0214\n",
      "Epoch 247/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.2005 - val_loss: 0.0216\n",
      "Epoch 248/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1840 - val_loss: 0.0217\n",
      "Epoch 249/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1922 - val_loss: 0.0214\n",
      "Epoch 250/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1848 - val_loss: 0.0213\n",
      "Epoch 251/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1781 - val_loss: 0.0212\n",
      "Epoch 252/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1928 - val_loss: 0.0210\n",
      "Epoch 253/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1902 - val_loss: 0.0208\n",
      "Epoch 254/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1953 - val_loss: 0.0209\n",
      "Epoch 255/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1777 - val_loss: 0.0212\n",
      "Epoch 256/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1824 - val_loss: 0.0216\n",
      "Epoch 257/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1837 - val_loss: 0.0213\n",
      "Epoch 258/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1826 - val_loss: 0.0210\n",
      "Epoch 259/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1850 - val_loss: 0.0207\n",
      "Epoch 260/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1942 - val_loss: 0.0208\n",
      "Epoch 261/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1904 - val_loss: 0.0211\n",
      "Epoch 262/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1886 - val_loss: 0.0211\n",
      "Epoch 263/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1717 - val_loss: 0.0211\n",
      "Epoch 264/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1833 - val_loss: 0.0205\n",
      "Epoch 265/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1921 - val_loss: 0.0206\n",
      "Epoch 266/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1858 - val_loss: 0.0210\n",
      "Epoch 267/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1857 - val_loss: 0.0210\n",
      "Epoch 268/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1803 - val_loss: 0.0212\n",
      "Epoch 269/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1871 - val_loss: 0.0208\n",
      "Epoch 270/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1891 - val_loss: 0.0206\n",
      "Epoch 271/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1885 - val_loss: 0.0209\n",
      "Epoch 272/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1796 - val_loss: 0.0214\n",
      "Epoch 273/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1868 - val_loss: 0.0212\n",
      "Epoch 274/2000\n",
      "169/169 [==============================] - 0s 3ms/sample - loss: 0.1826 - val_loss: 0.0212\n",
      "Epoch 275/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1704 - val_loss: 0.0210\n",
      "Epoch 276/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1937 - val_loss: 0.0206\n",
      "Epoch 277/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1940 - val_loss: 0.0206\n",
      "Epoch 278/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1850 - val_loss: 0.0208\n",
      "Epoch 279/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1895 - val_loss: 0.0208\n",
      "Epoch 280/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1958 - val_loss: 0.0211\n",
      "Epoch 281/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1903 - val_loss: 0.0213\n",
      "Epoch 282/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1844 - val_loss: 0.0210\n",
      "Epoch 283/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1825 - val_loss: 0.0213\n",
      "Epoch 284/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 0.1889 - val_loss: 0.0216\n",
      "Epoch 00284: early stopping\n",
      "score: 0.7821566670454037\n",
      "rmse: 0.2785162386680145\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    res = train_model(train_val_data,model_type[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 26)\n",
      "slice_window: (266, 6, 18) (266,)\n",
      "训练数据总量 (212, 6, 18)\n",
      "测试数据总量 (54, 6, 18)\n",
      "time_step: 6 feature_nums: 18\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "inputs (InputLayer)             [(None, 6, 18, 1)]   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 4, 16, 4)     40          inputs[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 256)          0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_3 (RepeatVector)  (None, 1, 256)       0           flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 256)          0           repeat_vector_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 256)          65792       flatten_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 1, 256)       0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "attention (Lambda)              (None, 256)          0           reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_4 (RepeatVector)  (None, 1, 256)       0           attention[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)           (None, 1, 256)       0           repeat_vector_3[0][0]            \n",
      "                                                                 repeat_vector_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_5 (Flatten)             (None, 256)          0           multiply_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_5 (RepeatVector)  (None, 3, 256)       0           flatten_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                   (None, 3, 128)       197120      repeat_vector_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_3 (LSTM)                   (None, 64)           49408       lstm_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 64)           0           lstm_3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 64)           4160        dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 1)            65          dense_4[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 316,585\n",
      "Trainable params: 316,585\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "Train on 169 samples, validate on 43 samples\n",
      "Epoch 1/2000\n",
      "169/169 [==============================] - 2s 12ms/sample - loss: 1.3269 - val_loss: 1.0987\n",
      "Epoch 2/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.3232 - val_loss: 1.0947\n",
      "Epoch 3/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.3189 - val_loss: 1.0902\n",
      "Epoch 4/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.3139 - val_loss: 1.0847\n",
      "Epoch 5/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.3078 - val_loss: 1.0779\n",
      "Epoch 6/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.3001 - val_loss: 1.0691\n",
      "Epoch 7/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.2899 - val_loss: 1.0575\n",
      "Epoch 8/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.2767 - val_loss: 1.0420\n",
      "Epoch 9/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.2590 - val_loss: 1.0212\n",
      "Epoch 10/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.2344 - val_loss: 0.9932\n",
      "Epoch 11/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.2024 - val_loss: 0.9558\n",
      "Epoch 12/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.1595 - val_loss: 0.9074\n",
      "Epoch 13/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 1.1056 - val_loss: 0.8469\n",
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     ]
    },
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     "text": [
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     ]
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "169/169 [==============================] - 0s 1ms/sample - loss: 0.1889 - val_loss: 0.0186\n",
      "Epoch 352/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.1941 - val_loss: 0.0187\n",
      "Epoch 353/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.1922 - val_loss: 0.0187\n",
      "Epoch 354/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.1753 - val_loss: 0.0186\n",
      "Epoch 355/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.1862 - val_loss: 0.0185\n",
      "Epoch 00355: early stopping\n",
      "score: 0.7889268240164196\n",
      "rmse: 0.2675446816588246\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    res = train_model(train_val_data,model_type[5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 26)\n",
      "slice_window: (266, 6, 18) (266,)\n",
      "训练数据总量 (212, 6, 18)\n",
      "测试数据总量 (54, 6, 18)\n",
      "time_step: 6 feature_nums: 18\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_4 (LSTM)                (None, 64)                21248     \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 25,473\n",
      "Trainable params: 25,473\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 169 samples, validate on 43 samples\n",
      "Epoch 1/2000\n",
      "169/169 [==============================] - 1s 8ms/sample - loss: 1.2266 - val_loss: 0.9439\n",
      "Epoch 2/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.1403 - val_loss: 0.8659\n",
      "Epoch 3/2000\n",
      "169/169 [==============================] - 0s 2ms/sample - loss: 1.0643 - val_loss: 0.7979\n",
      "Epoch 4/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.9970 - val_loss: 0.7384\n",
      "Epoch 5/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.9381 - val_loss: 0.6884\n",
      "Epoch 6/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.8886 - val_loss: 0.6475\n",
      "Epoch 7/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.8468 - val_loss: 0.6136\n",
      "Epoch 8/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.8114 - val_loss: 0.5856\n",
      "Epoch 9/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.7809 - val_loss: 0.5619\n",
      "Epoch 10/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.7540 - val_loss: 0.5411\n",
      "Epoch 11/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.7291 - val_loss: 0.5217\n",
      "Epoch 12/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.7053 - val_loss: 0.5022\n",
      "Epoch 13/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.6812 - val_loss: 0.4827\n",
      "Epoch 14/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.6566 - val_loss: 0.4621\n",
      "Epoch 15/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.6327 - val_loss: 0.4420\n",
      "Epoch 16/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.6097 - val_loss: 0.4226\n",
      "Epoch 17/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.5876 - val_loss: 0.4039\n",
      "Epoch 18/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.5661 - val_loss: 0.3856\n",
      "Epoch 19/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.5439 - val_loss: 0.3677\n",
      "Epoch 20/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.5221 - val_loss: 0.3497\n",
      "Epoch 21/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.5009 - val_loss: 0.3315\n",
      "Epoch 22/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.4804 - val_loss: 0.3142\n",
      "Epoch 23/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.4604 - val_loss: 0.2973\n",
      "Epoch 24/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.4413 - val_loss: 0.2805\n",
      "Epoch 25/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.4230 - val_loss: 0.2636\n",
      "Epoch 26/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.4052 - val_loss: 0.2469\n",
      "Epoch 27/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.3880 - val_loss: 0.2306\n",
      "Epoch 28/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.3714 - val_loss: 0.2143\n",
      "Epoch 29/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.3554 - val_loss: 0.1986\n",
      "Epoch 30/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.3400 - val_loss: 0.1832\n",
      "Epoch 31/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.3253 - val_loss: 0.1682\n",
      "Epoch 32/2000\n",
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      "Epoch 147/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.1847 - val_loss: 0.0268\n",
      "Epoch 148/2000\n",
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      "Epoch 150/2000\n",
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      "Epoch 151/2000\n",
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      "Epoch 152/2000\n",
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      "Epoch 153/2000\n",
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      "Epoch 154/2000\n",
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      "Epoch 156/2000\n",
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      "Epoch 165/2000\n",
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      "Epoch 166/2000\n",
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      "Epoch 167/2000\n",
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      "Epoch 168/2000\n",
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      "Epoch 169/2000\n",
      "169/169 [==============================] - 0s 1ms/sample - loss: 0.1814 - val_loss: 0.0271\n",
      "Epoch 00169: early stopping\n",
      "score: 0.7792733365390851\n",
      "rmse: 0.2832467801878193\n"
     ]
    },
    {
     "data": {
      "image/png": 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9+dWbW6nRbJgi0ol1nmQPXldOybstTnfc0M3nDmNXeSWLVu3pwMBEROKr8yX7o3vhUPjdMheO6cewvCwWLN2sxxaKSKfV+ZI9wM7lYW/i8xm3nDeMVbsO89bmsg4KTEQkvjpXsu833ru5auvSNm125aRB5GWnM//1zR0UmIhIfHWuZO9PgYJzYcuSNvXbp6f4+cL0At7YuJ/Vu/VQchHpfDpXsgcYPhPKd8DBrW3a7Lozh5KV5ufxpZpCQUQ6n86X7IfN9N43L2nTZj27pXLtmUN48aOPKTlY0QGBiYjET+dL9jnDoedgryunjb4wvRADfvVm234ViIgkus6X7M1g2AzvIm2gtk2bDuzVjTkTB/Ls8p0cPFbVMfGJiMRB50v24PXbHz8U9iyYoeadN4zK6lp+t2x7BwQmIhIfnTTZXwC+VFj3Yps3Pa1/D2aMzuOpt7dxvLptvwxERBJV50z23Xp5XTlrFrZpCGadW84bzv6jVfxpRUnUQxMRiYfOmewBxnwSyreH/fSqUNOG9eH0/J48vnQLtQFNoSAiya/zJvvTLgPzeWf3bWRm3HL+cLaVVbB4tSZIE5HkF1ayN7NZZrbezDaZ2Z3N1LnazNaY2Wozezq6YUYgKxeGToe1bU/2AJeM68/QnEzmL92iCdJEJOm1muzNzA88AswGxgJzzWxsgzojgbuA6c65ccBXox9qBMZ9CvZvgN3vt3lTv8+46dxhfLiznGVbDnRAcCIisRPOmf1UYJNzbotzrgp4FriiQZ2bgUeccwcBnHP7ohtmhMZfBSndYMVvItr8387IJy87nYf/vjHKgYmIxFY4yX4QEDpBfEmwLNQoYJSZ/dPMlpnZrKYaMrN5ZlZsZsWlpaWRRdwW3XrBuCvhoz+G/SDyUBmpfr50/nDe3lLGsi2a/lhEkle0LtCmACOBGcBc4HEz69WwknNugXOuyDlXlJeXF6WvbsXkz0HVEVj954g2v+7MIeRlp/PQqxuiG5eISAyFk+x3AYNDPucHy0KVAAudc9XOua3ABrzkH39DzoLcUfDeryPaPCPVz5fPH86yLQd4Ww83EZEkFU6yfxcYaWaFZpYGXAM0HOLyZ7yzeswsF69bJzHmCjaDKTd7z6bdsSyiJq49cwh9s9P5+asbNDJHRJJSq8neOVcD3Aa8DKwFnnPOrTaz+8xsTrDay0CZma0BlgB3OOcS5zR40nXQrQ+8+VBEm2ek+vnyjOEs33qAt9V3LyJJKKw+e+fcIufcKOfccOfcD4Nl9zjnFgaXnXPu6865sc65Cc65Zzsy6DZLy4Izb4ENL8G+tRE1MXfqEPr1SOehVzbq7F5Ekk7nvYO2oanzIDUT3ngwos0zUv3cOmMEy7ep715Ekk/XSfaZfWDqzbDyj/Bx2+fLAfjMlMH075GhvnsRSTpdJ9kDnPN1b+z9q/dGtHlGqp9bZw7n3W0HeUtn9yKSRLpWsu/WC867Azb/Azb9PaImri4Knt2/orN7EUkeXSvZA0y5CXoNgVfuhUCgzZtnpPr5yszhFG8/yD836exeRJJD10v2Kelwwb2wdyWsfC6iJq6eMpgBPdV3LyLJo+sle4Bxn4YBE+Ef90P18TZvnp7i59aZI3hv+0He2Lg/+vGJiERZ10z2Ph9c/AM4tBPemR9RE1cX5TOwZwY/U9+9iCSBrpnsAQrPg5GXeOPuj7W97z09xc/tF47kw53lvLx6bwcEKCISPV032QNcdJ83I+bSH0e0+VWT8xmWl8XPFq/Xs2pFJKF17WTf9zSYfAO8+0so29zmzVP8Pr558Wg27jvKC+83nAhURCRxdO1kDzDjLkjJiPhGq9nj+zNhUE9+/soGTtTURjk4EZHoULLP7gfTb4e1L0Y0BbKZccclo9lVXskz7+zogABFRNpPyR7grK9A9gB4+W6IYGTNuSNzmTasD/+9ZBPHTtR0QIAiIu2jZA/eFMj/8h3YVQyrX2jz5mbGt2edxv6jVfxiaWI8s0VEJJSSfZ3T50K/8fDq96DmRJs3nzSkN588fSALlm7m40OV0Y9PRKQdlOzr+PzeUMzy7bD88Yia+NYlowk4+Mnf1kc5OBGR9lGyDzXiAhh+gTfuvuJAmzcf3CeTG88p5H/e38VHJeXRj09EJEJK9g1dfD+cOAJLfxrR5rfOGE5u9zTu/9+1mkZBRBKGkn1D/cbCpOth+YKIbrTKzkjl6xeNZvm2A7y8ek8HBCgi0nZK9k2ZeTf40+Dv349o86uL8hndL5sfLlrL8WrdaCUi8adk35Ts/t6NVmv+EtGNVil+H/fOGcvOA5U8+lrbfx2IiESbkn1zzr6tXTdanT08lysnDmT+a5vZuv9YBwQoIhI+JfvmhN5otfL5iJr498vGkJ7i496Fq3WxVkTiKqxkb2azzGy9mW0ysztbqHeVmTkzK4peiHF0+lzviVaLv+ON0GmjvtkZfOPiUSzdUMpLq3SxVkTip9Vkb2Z+4BFgNjAWmGtmY5uolw3cDrwT7SDjxueHyx6Eo3vhtQciauL6aUMZO6AH9724hqOaN0dE4iScM/upwCbn3BbnXBXwLHBFE/V+APwIaPtDXRNZ/hkw+XOw7DHYu6bNm6f4fdz/qfHsOXycny3WnbUiEh/hJPtBwM6QzyXBspPMbDIw2Dn315YaMrN5ZlZsZsWlpaVtDjZuLrgXMnrAojsiulg7eUhvPjttKE++tY33trf9zlwRkfZq9wVaM/MBDwLfaK2uc26Bc67IOVeUl5fX3q+OnawcuOAe2P5mxBdrvz37NAb27Ma3nv9IY+9FJObCSfa7gMEhn/ODZXWygfHAa2a2DZgGLOw0F2nrTL4BBk7yLtYeP9zmzbunp/Afn57A5tJj/Nc/NnZAgCIizQsn2b8LjDSzQjNLA64BFtatdM4dcs7lOucKnHMFwDJgjnOuuEMijhefHy77mXex9vUfRdTE+aPyuGpyPvNf38KqXYeiHKCISPNaTfbOuRrgNuBlYC3wnHNutZndZ2ZzOjrAhDLoDDjjhogv1gJ89/Ix9M5M41vPf0RVTSDKAYqINC2sPnvn3CLn3Cjn3HDn3A+DZfc45xY2UXdGpzurD1V3sfav34joYm2vzDTuv3I8az4+rO4cEYkZ3UHbVpl94MLvw4634MNnImpi1vj+XDU5n0eWbNLoHBGJCSX7SEz6LAw+07tYG8FDTgC+N2csA3t142t/+FA3W4lIh1Oyj4TPB5f/HCrL4dV7I2oiOyOVB6+eyM6DFfzgxcj6/0VEwqVkH6l+4+Csr8CK30Q0DTLA1MI+fOn84fyheKcedCIiHUrJvj1m3Ak9B8P/fg1qqyNq4msXjmLcwB586/mP2HmgIsoBioh4lOzbIy0LZv8Y9q2BZY9G1kSKj0evm0zAOW79/QrdXSsiHULJvr1OuxRGX+bNilm+I6ImhuZk8eDVE1m56xDfV/+9iHQAJftomB28o/alb0fcxEVj+3HrjOE8s3wHfyze2foGIiJtoGQfDb0Gw4y7YP0iWP3niJv5+kWjOHt4Dt/58ypWlmg6BRGJHiX7aJn2ZRhwundn7bGyiJpI8ft4eO4kcrun84Unl7OjTBdsRSQ6lOyjxZ8KVzwKxw/BS9+KuJnc7uk89cWp1AQcN/x6OWVHT0QxSBHpqpTso6n/eDjvDlj1PKxr8TkuLRrRtzu/uqGI3eWVfPGpYiqqdIetiLSPkn20nft16DfBG3sf4VQKAGcM7cPDcyexsqScr/x+hWbIFJF2UbKPNn8qXPkIVJTBy//erqYuGdef+6+cwJL1pXz5d+9xokZj8EUkMkr2HWHA6XDO17xZMTe83K6mrj1zCD+4cjx/X7ePm3/zHsc0aZqIREDJvqOcdwfkjYEXb/cmTGuHz04bygOfnsCbG0uZ+/gy9uuirYi0kZJ9R0lJ97pzju6FxXe3u7lrpg7hF58tYsPeI8z5rzc1Dl9E2kTJviMNOgOm3w7v/w42vdru5i4a248/3nI2ZsZV89/it8u24yJ4WpaIdD1K9h3t/DshdzQsvB2OH253cxPye7LwtumcWdiH7/55FTc+Vczu8sooBCoinZmSfUdLzYArHoEju+GVe6LSZE73dJ76wlS+98mxvLV5Pxc++Dq/eH2zZswUkWYp2cfC4Ckw7VZ479ew5bWoNOnzGZ+fXsgrXzufs4bl8P9eWsfMn77Gb5dtp7JKSV9E6rN49fkWFRW54uLiuHx3XFRXwmPTIVANX34b0rtHtfm3Nu3nJ4vX8/6OcnpnpvKvZ+Qz5/RBjB/UAzOL6neJSPyY2XvOuaI2b6dkH0Pb34Zfz4YpN8FlP4168845lm89wK/e3Mo/1u2jJuAoyMnk8k8M5LxReUwc3Iu0FP2YE0lmSvbJ4qU74Z3H4PN/hYJzOuxryiuq+NuqPbz40W7e3lxGwEFGqo+ioX2YWtiHcQN7MHZgD/r3yNCZv0gS6dBkb2azgP8E/MAvnXMPNFj/deAmoAYoBb7onNveUptdNtlXHYPHzgYMvvRm1LtzmlJeUcU7Ww/w9uYy3t5cxvq9R06u652Zysi+2RTmZlGYl0VBTib9e3ajf48McrunkeLXLwGRRNJhyd7M/MAG4CKgBHgXmOucWxNSZybwjnOuwsy+DMxwzn2mpXa7bLIH2PZPePIymHQ9XPHfMf/6oydqWPfxYdZ8fJg1uw+zpfQYW/Yfa3Rnrs8gLzud/j0yyOmeTq9uqfTMTKVXtzR6Z6XSs1sqvTLT6B0s656RQmaan/QUn34tiHSQSJN9Shh1pgKbnHNbgl/0LHAFcDLZO+eWhNRfBlzf1kC6lILp3tw5bz4IIy+CsVfE9Ou7p6dQVNCHooI+9coPH69mR1kFew4dZ8/h4+w9fPzk8r4jx9mw9wjlFdUcbWV+Hp9BZloK3dL8ZKX56ZbmHQROvU597paWQlbIclqKj/Tgy1v2n/ycnuInPdVHmt9Heqr32e/TQUUkHOEk+0FA6ENRS4AzW6h/I/BSUyvMbB4wD2DIkCFhhthJzfx3bxjmwv/r3WnbMz/eEdEjI5Xxg3oyflDPFutV1wY4VFlNeUU1hyqrKK+o5mBFNcdO1HCsqobKqloqqmqpqKoJvnvLR0/UUHrkRIM67Rsm6vdZo4NDWoqPFJ+dfE/x+0j1G6l+Hyk+b/lkmc9HSnDdyfKT2wTLGrZRr05du97nNH9de4bf58Nvhj/Yht8X+u47+dmnA5bEQDjJPmxmdj1QBJzf1Hrn3AJgAXjdONH87qTjT4Wrfgnzz4UXvgSf+wv4/PGOKiypfh+53dPJ7Z7e7rYCAcfxGi/pV1bVcqImwImaWqpqAsHlQHC5lhPVAapqA5yorg0pb7p+da33qgk4qmsDnKgOcPR4DdW1jppAgOpar7wm+LmqxqtbU+uoqo39swMaHQz8vnqf/fXW+5qob/iswfpGBxkffh/1DjShbfvNO/CELtcdjPxm+H3gs2a2aVB+qh7BuHz4fIS3TfAA6dXjZD11DbZPOMl+FzA45HN+sKweM7sQuBs43zmnaRnDkTMcLv0J/OVW+Od/eg8+6WJ8Pgt260T1vKNdnHPUBtzJA0V1raOmNkB1IPh+ssxRHQhQXRNoVLcmcKqN2kCA2gDUBhqWe22cLHeO2lrXaNvQz4FAE+trHSeqT9WrPbk+UP+7WlifDFMs+YxGB4XGB6RTB7eT66z+wbCuPPSAVe8A1lJ5yIGo3neEftfJ+t7Btd57sM1heVmM6pcd0/0Xzv9h7wIjzawQL8lfA1wbWsHMJgG/AGY55/ZFPcrObOK1sOkVWPJDGHa+16UjcWXB5JDih4zU5Pi11V6BuoNNwBGoew9ArfMOCnXLgdADjzt14Ahdrg22FbpNTYN6AVf/ABbadq3j5AGy4XecrB+6Tb3vDm57so3Qg1uwjeCvusrq+rHVNmqrQXlt/fU1gciPkF86fzh3zj4tiv8FW9dqsnfO1ZjZbcDLeEMvn3DOrTaz+4Bi59xC4CdAd+CPwZ9aO5xzczow7s7DDC7/Oex8F/50E9zyRkyGY4qE8vkMH0YXObZFTcODTeivr9CDU8NfVX2y0mIeq26qShTb/glPXQ7jr4JPP+4dBEREGoh06KXumEkUBdNh5t2w8o+w7NF4RyMinYySfSI59xsw5pOw+Luw5fV4RyMinYiSfSIxgysfg5wR8PwXoHxHvCMSkU5CyT7RpGfDNU9DbTX84Xqoqoh3RCLSCSjZJ6LcEd4NV3tWemf4tS1PTyAi0hol+0Q16hK49Kew4W/wv1+FQOzv6hSRziNxbluUxqbcCEf2wNIfg/ng8ofAp+OziLSdkn2im/nv4ALwxk8hUAtzHk6aOXREJHEo2Sc6M/iX74AvBV5/AAI1cOWjSvgi0iZK9snADGbe5SX8JfdD1VH49AJIy4p3ZCKSJNQBnEzOvwNm/QjWL/IeXH6oJN4RiUiSULJPNtO+BHOfhbItMP8cWP+3eEckIklAyT4ZjboEbnnde7rVM5+Bv9wGlQfjHZWIJDAl+2SVMxxufBWm3w4fPA3/PQWWzYcaPTdGRBpTsk9mqRlw0X1w8z8g7zT427fh4cnw3lO661ZE6lGy7wwGToQbXoTP/hmy+8GL/xceGg//uB8ObotzcCKSCPTwks7GOdjwMrz7S9j0qlc27HwYMwdGzYKeg+Ibn4i0S6QPL1Gy78wOlcD7v4MPnzl1ht9/Aoy4CIZMg/wpkNknriGKSNso2UvznIPS9bDhJW+oZsm74Gq9dbmjYcAnvD7/vNOg7xjoXaA7dEUSVKTJXnfQdgVm0Pc073XO16DqGOxaATvfgZ3LYccy73GIdVIyoM8wyB4APQZ473WvrFzo1sf7RZDRSxOziSQJJfuuKC0LCs/1XnVOHPHO/vethdJ1cGALHPkY9q2Bo3u9ydgaMp+X8DNzvOSfng2pmV77qd0aLGdBWqZXlpoZXA6uO7mcAf408KXqICISZUr24knPhvwi79VQoBaO7vOSf8UBqDwAFWXeckVZ/c/VJd7TtapDXpHwpXiJ35966gBQtxxaXm85tcFyE3VD2zW/96vHfK28gnV8/pbXh9OGRaGNZuMI1hFpgpK9tM7n97pzegxo+7aBANQc95J+1bFTB4DQA8LJ5UoIVHuPZKytCr6aWq6uv1x1DGoP1q8bqGm8XaCL3HvQ5oOOAQ0OEk0eNCKo02RRU3UaloVTJ6jedUfXcnm9S5TNXa+04Ndb/e892V5oW67xukb1mlg3dZ4311UMKdlLx/L5vG6atEyvvz+eAoHgwSR4AHDO655q8eW8i9ktrg+njYD3C6m9bTQbS2tthKxvKo56mkiCjYqaqtNU8nQdV6de8g9ZbrW8ubp1390gYdf7rgYHAKyJdc3UO/nZIG80sRZWsjezWcB/An7gl865BxqsTwd+A5wBlAGfcc5ti26oIu3k84EvHVLS4x2JSMy1ehXMzPzAI8BsYCww18zGNqh2I3DQOTcC+Dnwo2gHKiIikQtnyMNUYJNzbotzrgp4FriiQZ0rgKeCy88DF5jpSpGISKIIJ9kPAnaGfC4JljVZxzlXAxwCcho2ZGbzzKzYzIpLS0sji1hERNospoOZnXMLnHNFzrmivLy8WH61iEiXFk6y3wUMDvmcHyxrso6ZpQA98S7UiohIAggn2b8LjDSzQjNLA64BFjaosxC4Ibj8r8A/XLwm3RERkUZaHXrpnKsxs9uAl/GGXj7hnFttZvcBxc65hcCvgN+a2SbgAN4BQUREEkRY4+ydc4uARQ3K7glZPg78W3RDExGRaInbFMdmVgpsj3DzXGB/FMOJlWSMWzHHTjLGrZhjIzTmoc65No9wiVuybw8zK45kPud4S8a4FXPsJGPcijk2ohGz5pEVEekClOxFRLqAZE32C+IdQISSMW7FHDvJGLdijo12x5yUffYiItI2yXpmLyIibaBkLyLSBSRdsjezWWa23sw2mdmd8Y6nKWY22MyWmNkaM1ttZrcHy79nZrvM7IPg69J4x9qQmW0zs5XB+IqDZX3M7BUz2xh87x3vOOuY2eiQ/fmBmR02s68m2r42syfMbJ+ZrQopa3K/mufh4N/4R2Y2OYFi/omZrQvG9YKZ9QqWF5hZZcj+nh+PmFuIu9m/BzO7K7iv15vZJQkU8x9C4t1mZh8EyyPb1865pHnhTdewGRgGpAEfAmPjHVcTcQ4AJgeXs4ENeA9++R7wzXjH10rs24DcBmU/Bu4MLt8J/Cjecbbw97EHGJpo+xo4D5gMrGptvwKXAi/hPctuGvBOAsV8MZASXP5RSMwFofUScF83+fcQ/P/yQyAdKAzmF38ixNxg/c+Ae9qzr5PtzD6cB6nEnXPuY+fciuDyEWAtjZ8BkExCH07zFHBl/EJp0QXAZudcpHdmdxjn3FK8eaNCNbdfrwB+4zzLgF5mFsHT3tunqZidc4ud98wKgGV4s+AmlGb2dXOuAJ51zp1wzm0FNuHlmZhqKebgg6CuBp5pz3ckW7IP50EqCcXMCoBJwDvBotuCP4GfSKTukBAOWGxm75nZvGBZP+fcx8HlPUC/+ITWqmuo/z9Eou/r5vZrsvydfxHvF0idQjN738xeN7Nz4xVUC5r6e0iGfX0usNc5tzGkrM37OtmSfVIxs+7An4CvOucOA48Bw4GJwMd4P80SzTnOucl4zxz+ipmdF7rSeb8jE268bnD67TnAH4NFybCvT0rU/docM7sbqAF+Hyz6GBjinJsEfB142sx6xCu+JiTV30MDc6l/EhPRvk62ZB/Og1QSgpml4iX63zvn/gfAObfXOVfrnAsAjxOHn4utcc7tCr7vA17Ai3FvXTdC8H1f/CJs1mxghXNuLyTHvqb5/ZrQf+dm9nngcuC64EGKYDdIWXD5Pby+71FxC7KBFv4eEn1fpwCfBv5QVxbpvk62ZB/Og1TiLtjH9itgrXPuwZDy0H7XTwGrGm4bT2aWZWbZdct4F+NWUf/hNDcAf4lPhC2qd/aT6Ps6qLn9uhD4XHBUzjTgUEh3T1yZ2SzgW8Ac51xFSHmemfmDy8OAkcCW+ETZWAt/DwuBa8ws3cwK8eJeHuv4WnAhsM45V1JXEPG+jvVV5yhctb4Ub3TLZuDueMfTTIzn4P0k/wj4IPi6FPgtsDJYvhAYEO9YG8Q9DG9kwofA6rr9i/fw+L8DG4FXgT7xjrVB3Fl4j8HsGVKWUPsa70D0MVCN1y98Y3P7FW8UziPBv/GVQFECxbwJr4+77u96frDuVcG/mQ+AFcAnE2xfN/v3ANwd3NfrgdmJEnOw/EngSw3qRrSvNV2CiEgXkGzdOCIiEgElexGRLkDJXkSkC1CyFxHpApTsRUS6ACV7EZEuQMleRKQL+P+W8QNsIUJLbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 42.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    res = train_model(train_val_data,model_type[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 26)\n",
      "slice_window: (266, 6, 18) (266,)\n",
      "训练数据总量 (212, 6, 18)\n",
      "测试数据总量 (54, 6, 18)\n",
      "time_step: 6 feature_nums: 18\n",
      "Model: \"sequential_16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_27 (Conv1D)           (None, 4, 128)            7040      \n",
      "_________________________________________________________________\n",
      "conv1d_28 (Conv1D)           (None, 2, 64)             24640     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_13 (MaxPooling (None, 1, 64)             0         \n",
      "_________________________________________________________________\n",
      "lstm_33 (LSTM)               (None, 1, 64)             33024     \n",
      "_________________________________________________________________\n",
      "lstm_34 (LSTM)               (None, 32)                12416     \n",
      "_________________________________________________________________\n",
      "dense_57 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_58 (Dense)             (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 78,209\n",
      "Trainable params: 78,209\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 169 samples, validate on 43 samples\n",
      "Epoch 1/500\n",
      "169/169 [==============================] - 3s 20ms/sample - loss: 1.3170 - val_loss: 1.0705\n",
      "Epoch 2/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 1.2527 - val_loss: 0.9332\n",
      "Epoch 3/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 1.0257 - val_loss: 0.6487\n",
      "Epoch 4/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.8313 - val_loss: 0.6306\n",
      "Epoch 5/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.8307 - val_loss: 0.6054\n",
      "Epoch 6/500\n",
      "169/169 [==============================] - 0s 324us/sample - loss: 0.8241 - val_loss: 0.6065\n",
      "Epoch 7/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.8207 - val_loss: 0.6040\n",
      "Epoch 8/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.8177 - val_loss: 0.6022\n",
      "Epoch 9/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8152 - val_loss: 0.5982\n",
      "Epoch 10/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.8092 - val_loss: 0.5907\n",
      "Epoch 11/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.7962 - val_loss: 0.5717\n",
      "Epoch 12/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.7649 - val_loss: 0.5264\n",
      "Epoch 13/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.6822 - val_loss: 0.3935\n",
      "Epoch 14/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.4999 - val_loss: 0.1933\n",
      "Epoch 15/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.3348 - val_loss: 0.1743\n",
      "Epoch 16/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.2869 - val_loss: 0.0719\n",
      "Epoch 17/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.2205 - val_loss: 0.0457\n",
      "Epoch 18/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.2147 - val_loss: 0.0616\n",
      "Epoch 19/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.2161 - val_loss: 0.0566\n",
      "Epoch 20/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.2073 - val_loss: 0.0444\n",
      "Epoch 21/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.2035 - val_loss: 0.0470\n",
      "Epoch 22/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.2044 - val_loss: 0.0466\n",
      "Epoch 23/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.2013 - val_loss: 0.0422\n",
      "Epoch 24/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1989 - val_loss: 0.0417\n",
      "Epoch 25/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1981 - val_loss: 0.0412\n",
      "Epoch 26/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1969 - val_loss: 0.0395\n",
      "Epoch 27/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1951 - val_loss: 0.0372\n",
      "Epoch 28/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1944 - val_loss: 0.0376\n",
      "Epoch 29/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1935 - val_loss: 0.0354\n",
      "Epoch 30/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1922 - val_loss: 0.0347\n",
      "Epoch 31/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1921 - val_loss: 0.0366\n",
      "Epoch 32/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1910 - val_loss: 0.0332\n",
      "Epoch 33/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1896 - val_loss: 0.0338\n",
      "Epoch 34/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1891 - val_loss: 0.0339\n",
      "Epoch 35/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1884 - val_loss: 0.0319\n",
      "Epoch 36/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1874 - val_loss: 0.0329\n",
      "Epoch 37/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1866 - val_loss: 0.0317\n",
      "Epoch 38/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1857 - val_loss: 0.0319\n",
      "Epoch 39/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1854 - val_loss: 0.0322\n",
      "Epoch 40/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1844 - val_loss: 0.0323\n",
      "Epoch 41/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1833 - val_loss: 0.0319\n",
      "Epoch 42/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1827 - val_loss: 0.0302\n",
      "Epoch 43/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1817 - val_loss: 0.0320\n",
      "Epoch 44/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1811 - val_loss: 0.0311\n",
      "Epoch 45/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1801 - val_loss: 0.0314\n",
      "Epoch 46/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1793 - val_loss: 0.0313\n",
      "Epoch 47/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1786 - val_loss: 0.0323\n",
      "Epoch 48/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1776 - val_loss: 0.0321\n",
      "Epoch 49/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1768 - val_loss: 0.0325\n",
      "Epoch 50/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1763 - val_loss: 0.0330\n",
      "Epoch 51/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1773 - val_loss: 0.0330\n",
      "Epoch 52/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1754 - val_loss: 0.0365\n",
      "Epoch 53/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1765 - val_loss: 0.0349\n",
      "Epoch 54/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1743 - val_loss: 0.0337\n",
      "Epoch 55/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1755 - val_loss: 0.0337\n",
      "Epoch 56/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1731 - val_loss: 0.0386\n",
      "Epoch 57/500\n",
      "169/169 [==============================] - 0s 349us/sample - loss: 0.1746 - val_loss: 0.0379\n",
      "Epoch 58/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1719 - val_loss: 0.0368\n",
      "Epoch 59/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1720 - val_loss: 0.0329\n",
      "Epoch 60/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1694 - val_loss: 0.0364\n",
      "Epoch 61/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1720 - val_loss: 0.0437\n",
      "Epoch 62/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1711 - val_loss: 0.0455\n",
      "Epoch 63/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1737 - val_loss: 0.0347\n",
      "Epoch 64/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1672 - val_loss: 0.0356\n",
      "Epoch 65/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1689 - val_loss: 0.0399\n",
      "Epoch 66/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1667 - val_loss: 0.0420\n",
      "Epoch 67/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1695 - val_loss: 0.0365\n",
      "Epoch 68/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1654 - val_loss: 0.0382\n",
      "Epoch 69/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1658 - val_loss: 0.0367\n",
      "Epoch 70/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1615 - val_loss: 0.0398\n",
      "Epoch 71/500\n",
      "169/169 [==============================] - 0s 361us/sample - loss: 0.1626 - val_loss: 0.0397\n",
      "Epoch 72/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1612 - val_loss: 0.0387\n",
      "Epoch 00072: early stopping\n",
      "score: 0.7513371875423911\n",
      "rmse: 0.3309603418818917\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.66 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    res = train_model(train_val_data,model_type[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 26)\n",
      "slice_window: (266, 6, 18) (266,)\n",
      "训练数据总量 (212, 6, 18)\n",
      "测试数据总量 (54, 6, 18)\n",
      "time_step: 6 feature_nums: 18\n",
      "Model: \"sequential_18\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_10 (Conv2D)           (None, 4, 16, 5)          50        \n",
      "_________________________________________________________________\n",
      "flatten_24 (Flatten)         (None, 320)               0         \n",
      "_________________________________________________________________\n",
      "repeat_vector_22 (RepeatVect (None, 6, 320)            0         \n",
      "_________________________________________________________________\n",
      "lstm_36 (LSTM)               (None, 64)                98560     \n",
      "_________________________________________________________________\n",
      "dense_61 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_62 (Dense)             (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 100,723\n",
      "Trainable params: 100,723\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 169 samples, validate on 43 samples\n",
      "Epoch 1/500\n",
      "169/169 [==============================] - 3s 20ms/sample - loss: 1.2940 - val_loss: 0.9679\n",
      "Epoch 2/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.9799 - val_loss: 0.6191\n",
      "Epoch 3/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.8225 - val_loss: 0.5854\n",
      "Epoch 4/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.7935 - val_loss: 0.5597\n",
      "Epoch 5/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.7349 - val_loss: 0.4680\n",
      "Epoch 6/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.5879 - val_loss: 0.2510\n",
      "Epoch 7/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.3738 - val_loss: 0.1146\n",
      "Epoch 8/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.2925 - val_loss: 0.0628\n",
      "Epoch 9/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.2256 - val_loss: 0.0514\n",
      "Epoch 10/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.2177 - val_loss: 0.0525\n",
      "Epoch 11/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.2163 - val_loss: 0.0380\n",
      "Epoch 12/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.2042 - val_loss: 0.0278\n",
      "Epoch 13/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.2017 - val_loss: 0.0305\n",
      "Epoch 14/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.2009 - val_loss: 0.0265\n",
      "Epoch 15/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.1960 - val_loss: 0.0240\n",
      "Epoch 16/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1942 - val_loss: 0.0249\n",
      "Epoch 17/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1933 - val_loss: 0.0235\n",
      "Epoch 18/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1912 - val_loss: 0.0224\n",
      "Epoch 19/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1898 - val_loss: 0.0224\n",
      "Epoch 20/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1889 - val_loss: 0.0221\n",
      "Epoch 21/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.1876 - val_loss: 0.0214\n",
      "Epoch 22/500\n",
      "169/169 [==============================] - 0s 511us/sample - loss: 0.1864 - val_loss: 0.0213\n",
      "Epoch 23/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1855 - val_loss: 0.0214\n",
      "Epoch 24/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1845 - val_loss: 0.0210\n",
      "Epoch 25/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.1836 - val_loss: 0.0210\n",
      "Epoch 26/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1827 - val_loss: 0.0210\n",
      "Epoch 27/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1819 - val_loss: 0.0211\n",
      "Epoch 28/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1811 - val_loss: 0.0212\n",
      "Epoch 29/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1802 - val_loss: 0.0215\n",
      "Epoch 30/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1794 - val_loss: 0.0218\n",
      "Epoch 31/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1785 - val_loss: 0.0221\n",
      "Epoch 32/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1776 - val_loss: 0.0225\n",
      "Epoch 33/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1767 - val_loss: 0.0230\n",
      "Epoch 34/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1759 - val_loss: 0.0234\n",
      "Epoch 35/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1750 - val_loss: 0.0235\n",
      "Epoch 36/500\n",
      "169/169 [==============================] - 0s 509us/sample - loss: 0.1741 - val_loss: 0.0239\n",
      "Epoch 37/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1732 - val_loss: 0.0241\n",
      "Epoch 38/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1721 - val_loss: 0.0244\n",
      "Epoch 39/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1711 - val_loss: 0.0245\n",
      "Epoch 40/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1701 - val_loss: 0.0250\n",
      "Epoch 41/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1691 - val_loss: 0.0259\n",
      "Epoch 42/500\n",
      "169/169 [==============================] - 0s 485us/sample - loss: 0.1681 - val_loss: 0.0267\n",
      "Epoch 43/500\n",
      "169/169 [==============================] - 0s 485us/sample - loss: 0.1668 - val_loss: 0.0275\n",
      "Epoch 44/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1659 - val_loss: 0.0283\n",
      "Epoch 45/500\n",
      "169/169 [==============================] - 0s 532us/sample - loss: 0.1644 - val_loss: 0.0281\n",
      "Epoch 46/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1677 - val_loss: 0.0300\n",
      "Epoch 47/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1619 - val_loss: 0.0331\n",
      "Epoch 48/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1672 - val_loss: 0.0304\n",
      "Epoch 49/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1642 - val_loss: 0.0392\n",
      "Epoch 50/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.1655 - val_loss: 0.0285\n",
      "Epoch 51/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.1613 - val_loss: 0.0302\n",
      "Epoch 52/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1640 - val_loss: 0.0349\n",
      "Epoch 53/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1663 - val_loss: 0.0392\n",
      "Epoch 54/500\n",
      "169/169 [==============================] - 0s 508us/sample - loss: 0.1590 - val_loss: 0.0327\n",
      "Epoch 55/500\n",
      "169/169 [==============================] - 0s 531us/sample - loss: 0.1560 - val_loss: 0.0330\n",
      "Epoch 00055: early stopping\n",
      "score: 0.7700336541300417\n",
      "rmse: 0.2986445392828533\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.68 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    res = train_model(train_val_data,model_type[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 26)\n",
      "slice_window: (266, 6, 18) (266,)\n",
      "训练数据总量 (212, 6, 18)\n",
      "测试数据总量 (54, 6, 18)\n",
      "time_step: 6 feature_nums: 18\n",
      "Model: \"model_11\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input1 (InputLayer)             [(None, 6, 18)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_32 (Conv1D)              (None, 4, 64)        3520        input1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "input2 (InputLayer)             [(None, 6, 18)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_16 (MaxPooling1D) (None, 2, 64)        0           conv1d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_34 (Conv1D)              (None, 2, 32)        2912        input2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_33 (Conv1D)              (None, 1, 32)        4128        max_pooling1d_16[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_17 (MaxPooling1D) (None, 1, 32)        0           conv1d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_11 (Concatenate)    (None, 1, 64)        0           conv1d_33[0][0]                  \n",
      "                                                                 max_pooling1d_17[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "flatten_26 (Flatten)            (None, 64)           0           concatenate_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_24 (RepeatVector) (None, 1, 64)        0           flatten_26[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lstm_39 (LSTM)                  (None, 1, 64)        33024       repeat_vector_24[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "lstm_40 (LSTM)                  (None, 32)           12416       lstm_39[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_65 (Dense)                (None, 32)           1056        lstm_40[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_66 (Dense)                (None, 1)            33          dense_65[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 57,089\n",
      "Trainable params: 57,089\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "Train on 169 samples, validate on 43 samples\n",
      "Epoch 1/500\n",
      "169/169 [==============================] - 4s 23ms/sample - loss: 1.3186 - val_loss: 1.0770\n",
      "Epoch 2/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 1.2791 - val_loss: 1.0037\n",
      "Epoch 3/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 1.1382 - val_loss: 0.7603\n",
      "Epoch 4/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8696 - val_loss: 0.6211\n",
      "Epoch 5/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8258 - val_loss: 0.5983\n",
      "Epoch 6/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8072 - val_loss: 0.5873\n",
      "Epoch 7/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.7804 - val_loss: 0.5458\n",
      "Epoch 8/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.6896 - val_loss: 0.4198\n",
      "Epoch 9/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.5290 - val_loss: 0.2613\n",
      "Epoch 10/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.3816 - val_loss: 0.1234\n",
      "Epoch 11/500\n",
      "169/169 [==============================] - 0s 416us/sample - loss: 0.2810 - val_loss: 0.0535\n",
      "Epoch 12/500\n",
      "169/169 [==============================] - 0s 416us/sample - loss: 0.2259 - val_loss: 0.0346\n",
      "Epoch 13/500\n",
      "169/169 [==============================] - 0s 374us/sample - loss: 0.2122 - val_loss: 0.0344\n",
      "Epoch 14/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.2082 - val_loss: 0.0299\n",
      "Epoch 15/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.2033 - val_loss: 0.0302\n",
      "Epoch 16/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.2007 - val_loss: 0.0297\n",
      "Epoch 17/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1986 - val_loss: 0.0295\n",
      "Epoch 18/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1974 - val_loss: 0.0294\n",
      "Epoch 19/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1962 - val_loss: 0.0295\n",
      "Epoch 20/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1950 - val_loss: 0.0296\n",
      "Epoch 21/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1942 - val_loss: 0.0297\n",
      "Epoch 22/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1930 - val_loss: 0.0299\n",
      "Epoch 23/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1923 - val_loss: 0.0302\n",
      "Epoch 24/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1915 - val_loss: 0.0304\n",
      "Epoch 25/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1908 - val_loss: 0.0309\n",
      "Epoch 26/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1901 - val_loss: 0.0312\n",
      "Epoch 27/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1895 - val_loss: 0.0315\n",
      "Epoch 28/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1888 - val_loss: 0.0319\n",
      "Epoch 29/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1883 - val_loss: 0.0322\n",
      "Epoch 30/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1878 - val_loss: 0.0325\n",
      "Epoch 31/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1873 - val_loss: 0.0328\n",
      "Epoch 32/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1867 - val_loss: 0.0332\n",
      "Epoch 33/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1863 - val_loss: 0.0336\n",
      "Epoch 34/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1858 - val_loss: 0.0338\n",
      "Epoch 35/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1854 - val_loss: 0.0342\n",
      "Epoch 36/500\n",
      "169/169 [==============================] - 0s 371us/sample - loss: 0.1850 - val_loss: 0.0344\n",
      "Epoch 37/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1846 - val_loss: 0.0347\n",
      "Epoch 38/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1842 - val_loss: 0.0349\n",
      "Epoch 39/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1839 - val_loss: 0.0351\n",
      "Epoch 40/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1835 - val_loss: 0.0354\n",
      "Epoch 41/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1832 - val_loss: 0.0356\n",
      "Epoch 42/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1829 - val_loss: 0.0358\n",
      "Epoch 43/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1826 - val_loss: 0.0361\n",
      "Epoch 44/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1823 - val_loss: 0.0363\n",
      "Epoch 45/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1820 - val_loss: 0.0365\n",
      "Epoch 46/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1817 - val_loss: 0.0367\n",
      "Epoch 47/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1814 - val_loss: 0.0369\n",
      "Epoch 48/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1811 - val_loss: 0.0371\n",
      "Epoch 00048: early stopping\n",
      "score: 0.7658116048048332\n",
      "rmse: 0.30580418699041473\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    res = train_model(train_val_data,model_type[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据总量 (272, 26)\n",
      "slice_window: (266, 6, 18) (266,)\n",
      "训练数据总量 (212, 6, 18)\n",
      "测试数据总量 (54, 6, 18)\n",
      "time_step: 6 feature_nums: 18\n",
      "Model: \"model_13\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input1 (InputLayer)             [(None, 6, 18)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_38 (Conv1D)              (None, 4, 32)        1760        input1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_39 (Conv1D)              (None, 2, 32)        3104        conv1d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "input2 (InputLayer)             [(None, 6, 18)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_19 (MaxPooling1D) (None, 1, 32)        0           conv1d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_40 (Conv1D)              (None, 1, 32)        3488        input2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lstm_43 (LSTM)                  (None, 32)           8320        max_pooling1d_19[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "lstm_44 (LSTM)                  (None, 32)           8320        conv1d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_13 (Concatenate)    (None, 64)           0           lstm_43[0][0]                    \n",
      "                                                                 lstm_44[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten_29 (Flatten)            (None, 64)           0           concatenate_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_70 (Dense)                (None, 64)           4160        flatten_29[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_10 (Reshape)            (None, 1, 64)        0           dense_70[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "attention (Lambda)              (None, 64)           0           reshape_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_26 (RepeatVector) (None, 1, 64)        0           attention[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_10 (Multiply)          (None, 1, 64)        0           concatenate_13[0][0]             \n",
      "                                                                 repeat_vector_26[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "flatten_30 (Flatten)            (None, 64)           0           multiply_10[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_71 (Dense)                (None, 64)           4160        flatten_30[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_72 (Dense)                (None, 1)            65          dense_71[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 33,377\n",
      "Trainable params: 33,377\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "Train on 169 samples, validate on 43 samples\n",
      "Epoch 1/500\n",
      "169/169 [==============================] - 5s 27ms/sample - loss: 1.3153 - val_loss: 1.0705\n",
      "Epoch 2/500\n",
      "169/169 [==============================] - 0s 416us/sample - loss: 1.2781 - val_loss: 1.0261\n",
      "Epoch 3/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 1.2247 - val_loss: 0.9605\n",
      "Epoch 4/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 1.1441 - val_loss: 0.8605\n",
      "Epoch 5/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 1.0265 - val_loss: 0.7357\n",
      "Epoch 6/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.9125 - val_loss: 0.6507\n",
      "Epoch 7/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8474 - val_loss: 0.6131\n",
      "Epoch 8/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8233 - val_loss: 0.6059\n",
      "Epoch 9/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8201 - val_loss: 0.6066\n",
      "Epoch 10/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8205 - val_loss: 0.6063\n",
      "Epoch 11/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8203 - val_loss: 0.6057\n",
      "Epoch 12/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8195 - val_loss: 0.6037\n",
      "Epoch 13/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.8147 - val_loss: 0.5916\n",
      "Epoch 14/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.7746 - val_loss: 0.5020\n",
      "Epoch 15/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.6464 - val_loss: 0.3747\n",
      "Epoch 16/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.4871 - val_loss: 0.1957\n",
      "Epoch 17/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.3381 - val_loss: 0.1077\n",
      "Epoch 18/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.2680 - val_loss: 0.0630\n",
      "Epoch 19/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.2279 - val_loss: 0.0377\n",
      "Epoch 20/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.2110 - val_loss: 0.0360\n",
      "Epoch 21/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.2067 - val_loss: 0.0360\n",
      "Epoch 22/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.2044 - val_loss: 0.0344\n",
      "Epoch 23/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.2025 - val_loss: 0.0339\n",
      "Epoch 24/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.2012 - val_loss: 0.0343\n",
      "Epoch 25/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.2000 - val_loss: 0.0343\n",
      "Epoch 26/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1988 - val_loss: 0.0344\n",
      "Epoch 27/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1978 - val_loss: 0.0345\n",
      "Epoch 28/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1969 - val_loss: 0.0348\n",
      "Epoch 29/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1960 - val_loss: 0.0348\n",
      "Epoch 30/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1951 - val_loss: 0.0346\n",
      "Epoch 31/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1942 - val_loss: 0.0345\n",
      "Epoch 32/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1934 - val_loss: 0.0341\n",
      "Epoch 33/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1926 - val_loss: 0.0338\n",
      "Epoch 34/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1919 - val_loss: 0.0337\n",
      "Epoch 35/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1912 - val_loss: 0.0335\n",
      "Epoch 36/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1905 - val_loss: 0.0335\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1899 - val_loss: 0.0335\n",
      "Epoch 38/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1893 - val_loss: 0.0335\n",
      "Epoch 39/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1886 - val_loss: 0.0332\n",
      "Epoch 40/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1880 - val_loss: 0.0332\n",
      "Epoch 41/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1875 - val_loss: 0.0331\n",
      "Epoch 42/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1869 - val_loss: 0.0329\n",
      "Epoch 43/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1863 - val_loss: 0.0327\n",
      "Epoch 44/500\n",
      "169/169 [==============================] - ETA: 0s - loss: 0.041 - 0s 370us/sample - loss: 0.1858 - val_loss: 0.0324\n",
      "Epoch 45/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1853 - val_loss: 0.0322\n",
      "Epoch 46/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1848 - val_loss: 0.0320\n",
      "Epoch 47/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1843 - val_loss: 0.0317\n",
      "Epoch 48/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1838 - val_loss: 0.0314\n",
      "Epoch 49/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1833 - val_loss: 0.0312\n",
      "Epoch 50/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1829 - val_loss: 0.0310\n",
      "Epoch 51/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1824 - val_loss: 0.0306\n",
      "Epoch 52/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1819 - val_loss: 0.0303\n",
      "Epoch 53/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1816 - val_loss: 0.0301\n",
      "Epoch 54/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1812 - val_loss: 0.0299\n",
      "Epoch 55/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1807 - val_loss: 0.0295\n",
      "Epoch 56/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1803 - val_loss: 0.0291\n",
      "Epoch 57/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1799 - val_loss: 0.0290\n",
      "Epoch 58/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1796 - val_loss: 0.0287\n",
      "Epoch 59/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1792 - val_loss: 0.0284\n",
      "Epoch 60/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1788 - val_loss: 0.0281\n",
      "Epoch 61/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1784 - val_loss: 0.0279\n",
      "Epoch 62/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1782 - val_loss: 0.0278\n",
      "Epoch 63/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1778 - val_loss: 0.0275\n",
      "Epoch 64/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1775 - val_loss: 0.0272\n",
      "Epoch 65/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1772 - val_loss: 0.0270\n",
      "Epoch 66/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1769 - val_loss: 0.0268\n",
      "Epoch 67/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1766 - val_loss: 0.0267\n",
      "Epoch 68/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1764 - val_loss: 0.0264\n",
      "Epoch 69/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1761 - val_loss: 0.0262\n",
      "Epoch 70/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1758 - val_loss: 0.0261\n",
      "Epoch 71/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1756 - val_loss: 0.0259\n",
      "Epoch 72/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1754 - val_loss: 0.0257\n",
      "Epoch 73/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1752 - val_loss: 0.0256\n",
      "Epoch 74/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1749 - val_loss: 0.0254\n",
      "Epoch 75/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1747 - val_loss: 0.0253\n",
      "Epoch 76/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1745 - val_loss: 0.0253\n",
      "Epoch 77/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1744 - val_loss: 0.0252\n",
      "Epoch 78/500\n",
      "169/169 [==============================] - 0s 346us/sample - loss: 0.1742 - val_loss: 0.0249\n",
      "Epoch 79/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1739 - val_loss: 0.0248\n",
      "Epoch 80/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1738 - val_loss: 0.0249\n",
      "Epoch 81/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1736 - val_loss: 0.0249\n",
      "Epoch 82/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1735 - val_loss: 0.0247\n",
      "Epoch 83/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1733 - val_loss: 0.0245\n",
      "Epoch 84/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1730 - val_loss: 0.0244\n",
      "Epoch 85/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1730 - val_loss: 0.0245\n",
      "Epoch 86/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1729 - val_loss: 0.0244\n",
      "Epoch 87/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1727 - val_loss: 0.0243\n",
      "Epoch 88/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1725 - val_loss: 0.0242\n",
      "Epoch 89/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1724 - val_loss: 0.0242\n",
      "Epoch 90/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1723 - val_loss: 0.0242\n",
      "Epoch 91/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1722 - val_loss: 0.0241\n",
      "Epoch 92/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1720 - val_loss: 0.0240\n",
      "Epoch 93/500\n",
      "169/169 [==============================] - 0s 371us/sample - loss: 0.1718 - val_loss: 0.0241\n",
      "Epoch 94/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1717 - val_loss: 0.0241\n",
      "Epoch 95/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1716 - val_loss: 0.0240\n",
      "Epoch 96/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1715 - val_loss: 0.0239\n",
      "Epoch 97/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1712 - val_loss: 0.0238\n",
      "Epoch 98/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1711 - val_loss: 0.0239\n",
      "Epoch 99/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1710 - val_loss: 0.0239\n",
      "Epoch 100/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1709 - val_loss: 0.0239\n",
      "Epoch 101/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1708 - val_loss: 0.0238\n",
      "Epoch 102/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1706 - val_loss: 0.0238\n",
      "Epoch 103/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1705 - val_loss: 0.0238\n",
      "Epoch 104/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1704 - val_loss: 0.0238\n",
      "Epoch 105/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1703 - val_loss: 0.0238\n",
      "Epoch 106/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1701 - val_loss: 0.0238\n",
      "Epoch 107/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1700 - val_loss: 0.0238\n",
      "Epoch 108/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1699 - val_loss: 0.0238\n",
      "Epoch 109/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1697 - val_loss: 0.0238\n",
      "Epoch 110/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1697 - val_loss: 0.0239\n",
      "Epoch 111/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1696 - val_loss: 0.0238\n",
      "Epoch 112/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1695 - val_loss: 0.0238\n",
      "Epoch 113/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1693 - val_loss: 0.0238\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 114/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1692 - val_loss: 0.0239\n",
      "Epoch 115/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1691 - val_loss: 0.0239\n",
      "Epoch 116/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1691 - val_loss: 0.0239\n",
      "Epoch 117/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1689 - val_loss: 0.0239\n",
      "Epoch 118/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1688 - val_loss: 0.0239\n",
      "Epoch 119/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1687 - val_loss: 0.0240\n",
      "Epoch 120/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1686 - val_loss: 0.0240\n",
      "Epoch 121/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1685 - val_loss: 0.0240\n",
      "Epoch 122/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1684 - val_loss: 0.0241\n",
      "Epoch 123/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1683 - val_loss: 0.0241\n",
      "Epoch 124/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1682 - val_loss: 0.0241\n",
      "Epoch 125/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1681 - val_loss: 0.0242\n",
      "Epoch 126/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1680 - val_loss: 0.0242\n",
      "Epoch 127/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1679 - val_loss: 0.0243\n",
      "Epoch 128/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1678 - val_loss: 0.0243\n",
      "Epoch 129/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1677 - val_loss: 0.0243\n",
      "Epoch 130/500\n",
      "169/169 [==============================] - 0s 393us/sample - loss: 0.1676 - val_loss: 0.0244\n",
      "Epoch 131/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1675 - val_loss: 0.0244\n",
      "Epoch 132/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1674 - val_loss: 0.0245\n",
      "Epoch 133/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1673 - val_loss: 0.0245\n",
      "Epoch 134/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1672 - val_loss: 0.0246\n",
      "Epoch 135/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1671 - val_loss: 0.0246\n",
      "Epoch 136/500\n",
      "169/169 [==============================] - 0s 370us/sample - loss: 0.1670 - val_loss: 0.0246\n",
      "Epoch 137/500\n",
      "169/169 [==============================] - 0s 369us/sample - loss: 0.1669 - val_loss: 0.0247\n",
      "Epoch 138/500\n",
      "169/169 [==============================] - 0s 347us/sample - loss: 0.1668 - val_loss: 0.0248\n",
      "Epoch 00138: early stopping\n",
      "score: 0.7434227876004083\n",
      "rmse: 0.34512960414861893\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 15.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    model_type = ['lstm','conv1d_lstm','conv2d_lstm','dou_cnn_lstm','cnn_lstm_att','conv2d_lstm_att']\n",
    "    res = train_model(train_val_data,model_type[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| model|optimizer |lr |rmse |loss |val_loss |early_stop_time |score|\n",
    "|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n",
    "| lstm| adam | 0.001 |0.3067|0.0507 |0.0941 |53min 4s|0.765|\n",
    "| conv1d+lstm| adam | 0.001 |0.2781|0.0416 |0.0774 |24min 25s|0.782|\n",
    "| conv2d+lstm| adam | / |0.2748|0.0109 |0.0755 |22min 32s|0.7844|\n",
    "| dou_conv1d+lstm| adam | / |0.2948|0.0209 |0.0955 |39min |0.77|\n",
    "| conv1d+lstm+attention| adam | / |0.2948|0.0357| 0.0969 |39min |0.77|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tf1.14]",
   "language": "python",
   "name": "conda-env-tf1.14-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
